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<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN">
<html><head><title>Python: module main</title>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
</head><body bgcolor="#f0f0f8">
<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="heading">
<tr bgcolor="#7799ee">
<td valign=bottom> <br>
<font color="#ffffff" face="helvetica, arial"> <br><big><big><strong>main</strong></big></big></font></td
><td align=right valign=bottom
><font color="#ffffff" face="helvetica, arial"><a href=".">index</a><br><a href="file:/Users/liuhaohe/projects/torchsubband/torchsubband/main.py">/Users/liuhaohe/projects/torchsubband/torchsubband/main.py</a></font></td></tr></table>
<p><tt>@File : main.py <br>
@Contact : [email protected]<br>
@License : (C)Copyright 2020-2100<br>
<br>
@Modify Time @Author @Version @Desciption<br>
------------ ------- -------- -----------<br>
9/19/21 4:07 PM Haohe Liu 1.0 None</tt></p>
<p>
<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="section">
<tr bgcolor="#aa55cc">
<td colspan=3 valign=bottom> <br>
<font color="#ffffff" face="helvetica, arial"><big><strong>Modules</strong></big></font></td></tr>
<tr><td bgcolor="#aa55cc"><tt> </tt></td><td> </td>
<td width="100%"><table width="100%" summary="list"><tr><td width="25%" valign=top><a href="torch.nn.functional.html">torch.nn.functional</a><br>
</td><td width="25%" valign=top><a href="torch.nn.html">torch.nn</a><br>
</td><td width="25%" valign=top><a href="numpy.html">numpy</a><br>
</td><td width="25%" valign=top><a href="torch.html">torch</a><br>
</td></tr></table></td></tr></table><p>
<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="section">
<tr bgcolor="#ee77aa">
<td colspan=3 valign=bottom> <br>
<font color="#ffffff" face="helvetica, arial"><big><strong>Classes</strong></big></font></td></tr>
<tr><td bgcolor="#ee77aa"><tt> </tt></td><td> </td>
<td width="100%"><dl>
<dt><font face="helvetica, arial"><a href="torch.nn.modules.module.html#Module">torch.nn.modules.module.Module</a>(<a href="builtins.html#object">builtins.object</a>)
</font></dt><dd>
<dl>
<dt><font face="helvetica, arial"><a href="main.html#SubbandDSP">SubbandDSP</a>
</font></dt></dl>
</dd>
</dl>
<p>
<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="section">
<tr bgcolor="#ffc8d8">
<td colspan=3 valign=bottom> <br>
<font color="#000000" face="helvetica, arial"><a name="SubbandDSP">class <strong>SubbandDSP</strong></a>(<a href="torch.nn.modules.module.html#Module">torch.nn.modules.module.Module</a>)</font></td></tr>
<tr bgcolor="#ffc8d8"><td rowspan=2><tt> </tt></td>
<td colspan=2><tt><a href="#SubbandDSP">SubbandDSP</a>(subband=2, window_size=2048, hop_size=441)<br>
<br>
Base class for all neural network modules.<br>
<br>
Your models should also subclass this class.<br>
<br>
Modules can also contain other Modules, allowing to nest them in<br>
a tree structure. You can assign the submodules as regular attributes::<br>
<br>
import torch.nn as nn<br>
import torch.nn.functional as F<br>
<br>
class Model(nn.<a href="torch.nn.modules.module.html#Module">Module</a>):<br>
def <a href="#SubbandDSP-__init__">__init__</a>(self):<br>
super(Model, self).<a href="#SubbandDSP-__init__">__init__</a>()<br>
self.<strong>conv1</strong> = nn.Conv2d(1, 20, 5)<br>
self.<strong>conv2</strong> = nn.Conv2d(20, 20, 5)<br>
<br>
def <a href="#SubbandDSP-forward">forward</a>(self, x):<br>
x = F.relu(self.conv1(x))<br>
return F.relu(self.conv2(x))<br>
<br>
Submodules assigned in this way will be registered, and will have their<br>
parameters converted too when you call :meth:`to`, etc.<br>
<br>
:ivar training: Boolean represents whether this module is in training or<br>
evaluation mode.<br>
:vartype training: bool<br> </tt></td></tr>
<tr><td> </td>
<td width="100%"><dl><dt>Method resolution order:</dt>
<dd><a href="main.html#SubbandDSP">SubbandDSP</a></dd>
<dd><a href="torch.nn.modules.module.html#Module">torch.nn.modules.module.Module</a></dd>
<dd><a href="builtins.html#object">builtins.object</a></dd>
</dl>
<hr>
Methods defined here:<br>
<dl><dt><a name="SubbandDSP-__init__"><strong>__init__</strong></a>(self, subband=2, window_size=2048, hop_size=441)</dt><dd><tt>Args:<br>
subband: int, [1,2,4,8]. The subband number you wanna divide. 'subbband==1' means do not need subband.<br>
window_size: stft parameter<br>
hop_size: stft parameter</tt></dd></dl>
<dl><dt><a name="SubbandDSP-complex_sub_spec_to_wav"><strong>complex_sub_spec_to_wav</strong></a>(self, sps, length)</dt><dd><tt>The reverse function of wav_to_complex_subband_spectrogram. Convert complex spectrogram into waveform.<br>
Args:<br>
sps: tensor, complex as channel spectrogram, (batch_size, 2 * channels_num * subband_num, time_steps, freq_bins // subband_num),<br>
length: int, expect sample length of the output tensor<br>
<br>
Returns:<br>
(batch_size, channels_num, samples)</tt></dd></dl>
<dl><dt><a name="SubbandDSP-mag_phase_sub_spec_to_wav"><strong>mag_phase_sub_spec_to_wav</strong></a>(self, sps, coss, sins, length)</dt><dd><tt>The reverse functino of wav_to_mag_phase_subband_spectrogram. Convert subband magnutde spectrogram and its subband phases into fullband waveform.<br>
Args:<br>
sps: tensor, magnitude spectrogram (batch_size, channels_num * subband_num, time_steps, freq_bins // subband_num),<br>
coss: tensor, (batch_size, channels_num * subband_num, time_steps, freq_bins // subband_num)<br>
sins: tensor, (batch_size, channels_num * subband_num, time_steps, freq_bins // subband_num)<br>
length: int, expect sample length of the output tensor<br>
Returns:<br>
tensor, (batch_size, channels_num, samples)</tt></dd></dl>
<dl><dt><a name="SubbandDSP-spectrogram_phase_to_wav"><strong>spectrogram_phase_to_wav</strong></a>(self, sps, coss, sins, length)</dt><dd><tt>The reverse function of wav_to_spectrogram_phase. Convert magnitude spectrogram and phase to waveform.<br>
Args:<br>
sps: tensor, magnitude spectrogram, (batch_size, channels_num, time_steps, freq_bins),<br>
coss: tensor, phase angle, (batch_size, channels_num, time_steps, freq_bins)<br>
sins: tensor, phase angle, (batch_size, channels_num, time_steps, freq_bins)<br>
length: int, expect sample length of the output tensor<br>
Returns:<br>
output: tensor, (batch_size, channels_num, samples)</tt></dd></dl>
<dl><dt><a name="SubbandDSP-sub_to_wav"><strong>sub_to_wav</strong></a>(self, subwav, length)</dt><dd><tt>The reverse function of wav_to_subband.<br>
Args:<br>
subwav: tensor, (batch_size, channels_num * subband_nums, <a href="#-ceil">ceil</a>(samples / subbandnum))<br>
length: int, expect sample length of the output tensor<br>
<br>
Returns:<br>
tensor, (batch_size, channels_num, samples)</tt></dd></dl>
<dl><dt><a name="SubbandDSP-wav_to_complex_sub_spec"><strong>wav_to_complex_sub_spec</strong></a>(self, input)</dt><dd><tt>Convert waveform in each channel to several complex subband spectrogram. The real and imaginary parts are stored separately in different channels.<br>
Args:<br>
input: tensor, (batch_size, channels_num, samples)<br>
<br>
Returns:<br>
tensor, complex as channel spectrogram, (batch_size, 2 * channels_num * subband_num, time_steps, freq_bins // subband_num),</tt></dd></dl>
<dl><dt><a name="SubbandDSP-wav_to_mag_phase_sub_spec"><strong>wav_to_mag_phase_sub_spec</strong></a>(self, input)</dt><dd><tt>Convert the input waveform to its subband spectrograms, which are concatenated in the channel dimension.<br>
Args:<br>
input: (batch_size, channels_num, samples)<br>
Returns:<br>
magnitude spectrogram (batch_size, channels_num * subband_num, time_steps, freq_bins // subband_num),<br>
coss: (batch_size, channels_num * subband_num, time_steps, freq_bins // subband_num)<br>
sins: (batch_size, channels_num * subband_num, time_steps, freq_bins // subband_num)</tt></dd></dl>
<dl><dt><a name="SubbandDSP-wav_to_spectrogram_phase"><strong>wav_to_spectrogram_phase</strong></a>(self, input)</dt><dd><tt>Convert input waveform to magnitude spectrogram and phases.<br>
Args:<br>
input: (batch_size, channels_num, samples)<br>
Returns:<br>
magnitude spectrogram (batch_size, channels_num, time_steps, freq_bins),<br>
phase angle cos: (batch_size, channels_num, time_steps, freq_bins)<br>
phase angle sin: (batch_size, channels_num, time_steps, freq_bins)</tt></dd></dl>
<dl><dt><a name="SubbandDSP-wav_to_sub"><strong>wav_to_sub</strong></a>(self, input)</dt><dd><tt>Convert input waveform into several subband signals<br>
Args:<br>
input: tensor, (batch_size, channels_num, samples)<br>
Returns:<br>
tensor, (batch_size, channels_num * subbandnum, <a href="#-ceil">ceil</a>(samples / subbandnum))</tt></dd></dl>
<hr>
Methods inherited from <a href="torch.nn.modules.module.html#Module">torch.nn.modules.module.Module</a>:<br>
<dl><dt><a name="SubbandDSP-__call__"><strong>__call__</strong></a> = <a href="#SubbandDSP-_call_impl">_call_impl</a>(self, *input, **kwargs)</dt></dl>
<dl><dt><a name="SubbandDSP-__delattr__"><strong>__delattr__</strong></a>(self, name)</dt><dd><tt>Implement delattr(self, name).</tt></dd></dl>
<dl><dt><a name="SubbandDSP-__dir__"><strong>__dir__</strong></a>(self)</dt><dd><tt>Default dir() implementation.</tt></dd></dl>
<dl><dt><a name="SubbandDSP-__getattr__"><strong>__getattr__</strong></a>(self, name: str) -> Union[torch.Tensor, ForwardRef('Module')]</dt></dl>
<dl><dt><a name="SubbandDSP-__repr__"><strong>__repr__</strong></a>(self)</dt><dd><tt>Return repr(self).</tt></dd></dl>
<dl><dt><a name="SubbandDSP-__setattr__"><strong>__setattr__</strong></a>(self, name: str, value: Union[torch.Tensor, ForwardRef('Module')]) -> None</dt><dd><tt>Implement setattr(self, name, value).</tt></dd></dl>
<dl><dt><a name="SubbandDSP-__setstate__"><strong>__setstate__</strong></a>(self, state)</dt></dl>
<dl><dt><a name="SubbandDSP-add_module"><strong>add_module</strong></a>(self, name: str, module: Union[ForwardRef('Module'), NoneType]) -> None</dt><dd><tt>Adds a child module to the current module.<br>
<br>
The module can be accessed as an attribute using the given name.<br>
<br>
Args:<br>
name (string): name of the child module. The child module can be<br>
accessed from this module using the given name<br>
module (<a href="torch.nn.modules.module.html#Module">Module</a>): child module to be added to the module.</tt></dd></dl>
<dl><dt><a name="SubbandDSP-apply"><strong>apply</strong></a>(self: ~T, fn: Callable[[ForwardRef('Module')], NoneType]) -> ~T</dt><dd><tt>Applies ``fn`` recursively to every submodule (as returned by ``.<a href="#SubbandDSP-children">children</a>()``)<br>
as well as self. Typical use includes initializing the parameters of a model<br>
(see also :ref:`nn-init-doc`).<br>
<br>
Args:<br>
fn (:class:`<a href="torch.nn.modules.module.html#Module">Module</a>` -> None): function to be applied to each submodule<br>
<br>
Returns:<br>
<a href="torch.nn.modules.module.html#Module">Module</a>: self<br>
<br>
Example::<br>
<br>
>>> @torch.no_grad()<br>
>>> def init_weights(m):<br>
>>> print(m)<br>
>>> if <a href="#SubbandDSP-type">type</a>(m) == nn.Linear:<br>
>>> m.weight.fill_(1.0)<br>
>>> print(m.weight)<br>
>>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))<br>
>>> net.<a href="#SubbandDSP-apply">apply</a>(init_weights)<br>
Linear(in_features=2, out_features=2, bias=True)<br>
Parameter containing:<br>
tensor([[ 1., 1.],<br>
[ 1., 1.]])<br>
Linear(in_features=2, out_features=2, bias=True)<br>
Parameter containing:<br>
tensor([[ 1., 1.],<br>
[ 1., 1.]])<br>
Sequential(<br>
(0): Linear(in_features=2, out_features=2, bias=True)<br>
(1): Linear(in_features=2, out_features=2, bias=True)<br>
)<br>
Sequential(<br>
(0): Linear(in_features=2, out_features=2, bias=True)<br>
(1): Linear(in_features=2, out_features=2, bias=True)<br>
)</tt></dd></dl>
<dl><dt><a name="SubbandDSP-bfloat16"><strong>bfloat16</strong></a>(self: ~T) -> ~T</dt><dd><tt>Casts all floating point parameters and buffers to ``bfloat16`` datatype.<br>
<br>
.. note::<br>
This method modifies the module in-place.<br>
<br>
Returns:<br>
<a href="torch.nn.modules.module.html#Module">Module</a>: self</tt></dd></dl>
<dl><dt><a name="SubbandDSP-buffers"><strong>buffers</strong></a>(self, recurse: bool = True) -> Iterator[torch.Tensor]</dt><dd><tt>Returns an iterator over module buffers.<br>
<br>
Args:<br>
recurse (bool): if True, then yields buffers of this module<br>
and all submodules. Otherwise, yields only buffers that<br>
are direct members of this module.<br>
<br>
Yields:<br>
torch.Tensor: module buffer<br>
<br>
Example::<br>
<br>
>>> for buf in model.<a href="#SubbandDSP-buffers">buffers</a>():<br>
>>> print(<a href="#SubbandDSP-type">type</a>(buf), buf.size())<br>
<class 'torch.Tensor'> (20L,)<br>
<class 'torch.Tensor'> (20L, 1L, 5L, 5L)</tt></dd></dl>
<dl><dt><a name="SubbandDSP-children"><strong>children</strong></a>(self) -> Iterator[ForwardRef('Module')]</dt><dd><tt>Returns an iterator over immediate children modules.<br>
<br>
Yields:<br>
<a href="torch.nn.modules.module.html#Module">Module</a>: a child module</tt></dd></dl>
<dl><dt><a name="SubbandDSP-cpu"><strong>cpu</strong></a>(self: ~T) -> ~T</dt><dd><tt>Moves all model parameters and buffers to the CPU.<br>
<br>
.. note::<br>
This method modifies the module in-place.<br>
<br>
Returns:<br>
<a href="torch.nn.modules.module.html#Module">Module</a>: self</tt></dd></dl>
<dl><dt><a name="SubbandDSP-cuda"><strong>cuda</strong></a>(self: ~T, device: Union[int, torch.device, NoneType] = None) -> ~T</dt><dd><tt>Moves all model parameters and buffers to the GPU.<br>
<br>
This also makes associated parameters and buffers different objects. So<br>
it should be called before constructing optimizer if the module will<br>
live on GPU while being optimized.<br>
<br>
.. note::<br>
This method modifies the module in-place.<br>
<br>
Args:<br>
device (int, optional): if specified, all parameters will be<br>
copied to that device<br>
<br>
Returns:<br>
<a href="torch.nn.modules.module.html#Module">Module</a>: self</tt></dd></dl>
<dl><dt><a name="SubbandDSP-double"><strong>double</strong></a>(self: ~T) -> ~T</dt><dd><tt>Casts all floating point parameters and buffers to ``double`` datatype.<br>
<br>
.. note::<br>
This method modifies the module in-place.<br>
<br>
Returns:<br>
<a href="torch.nn.modules.module.html#Module">Module</a>: self</tt></dd></dl>
<dl><dt><a name="SubbandDSP-eval"><strong>eval</strong></a>(self: ~T) -> ~T</dt><dd><tt>Sets the module in evaluation mode.<br>
<br>
This has any effect only on certain modules. See documentations of<br>
particular modules for details of their behaviors in training/evaluation<br>
mode, if they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`,<br>
etc.<br>
<br>
This is equivalent with :meth:`self.<a href="#SubbandDSP-train">train</a>(False) <torch.nn.<a href="torch.nn.modules.module.html#Module">Module</a>.train>`.<br>
<br>
See :ref:`locally-disable-grad-doc` for a comparison between<br>
`.<a href="#SubbandDSP-eval">eval</a>()` and several similar mechanisms that may be confused with it.<br>
<br>
Returns:<br>
<a href="torch.nn.modules.module.html#Module">Module</a>: self</tt></dd></dl>
<dl><dt><a name="SubbandDSP-extra_repr"><strong>extra_repr</strong></a>(self) -> str</dt><dd><tt>Set the extra representation of the module<br>
<br>
To print customized extra information, you should re-implement<br>
this method in your own modules. Both single-line and multi-line<br>
strings are acceptable.</tt></dd></dl>
<dl><dt><a name="SubbandDSP-float"><strong>float</strong></a>(self: ~T) -> ~T</dt><dd><tt>Casts all floating point parameters and buffers to ``float`` datatype.<br>
<br>
.. note::<br>
This method modifies the module in-place.<br>
<br>
Returns:<br>
<a href="torch.nn.modules.module.html#Module">Module</a>: self</tt></dd></dl>
<dl><dt><a name="SubbandDSP-forward"><strong>forward</strong></a> = _forward_unimplemented(self, *input: Any) -> None</dt><dd><tt>Defines the computation performed at every call.<br>
<br>
Should be overridden by all subclasses.<br>
<br>
.. note::<br>
Although the recipe for forward pass needs to be defined within<br>
this function, one should call the :class:`<a href="torch.nn.modules.module.html#Module">Module</a>` instance afterwards<br>
instead of this since the former takes care of running the<br>
registered hooks while the latter silently ignores them.</tt></dd></dl>
<dl><dt><a name="SubbandDSP-get_buffer"><strong>get_buffer</strong></a>(self, target: str) -> 'Tensor'</dt><dd><tt>Returns the buffer given by ``target`` if it exists,<br>
otherwise throws an error.<br>
<br>
See the docstring for ``get_submodule`` for a more detailed<br>
explanation of this method's functionality as well as how to<br>
correctly specify ``target``.<br>
<br>
Args:<br>
target: The fully-qualified string name of the buffer<br>
to look for. (See ``get_submodule`` for how to specify a<br>
fully-qualified string.)<br>
<br>
Returns:<br>
torch.Tensor: The buffer referenced by ``target``<br>
<br>
Raises:<br>
AttributeError: If the target string references an invalid<br>
path or resolves to something that is not a<br>
buffer</tt></dd></dl>
<dl><dt><a name="SubbandDSP-get_parameter"><strong>get_parameter</strong></a>(self, target: str) -> 'Parameter'</dt><dd><tt>Returns the parameter given by ``target`` if it exists,<br>
otherwise throws an error.<br>
<br>
See the docstring for ``get_submodule`` for a more detailed<br>
explanation of this method's functionality as well as how to<br>
correctly specify ``target``.<br>
<br>
Args:<br>
target: The fully-qualified string name of the Parameter<br>
to look for. (See ``get_submodule`` for how to specify a<br>
fully-qualified string.)<br>
<br>
Returns:<br>
torch.nn.Parameter: The Parameter referenced by ``target``<br>
<br>
Raises:<br>
AttributeError: If the target string references an invalid<br>
path or resolves to something that is not an<br>
``nn.Parameter``</tt></dd></dl>
<dl><dt><a name="SubbandDSP-get_submodule"><strong>get_submodule</strong></a>(self, target: str) -> 'Module'</dt><dd><tt>Returns the submodule given by ``target`` if it exists,<br>
otherwise throws an error.<br>
<br>
For example, let's say you have an ``nn.<a href="torch.nn.modules.module.html#Module">Module</a>`` ``A`` that<br>
looks like this:<br>
<br>
.. code-block::text<br>
<br>
A(<br>
(net_b): <a href="torch.nn.modules.module.html#Module">Module</a>(<br>
(net_c): <a href="torch.nn.modules.module.html#Module">Module</a>(<br>
(conv): Conv2d(16, 33, kernel_size=(3, 3), stride=(2, 2))<br>
)<br>
(linear): Linear(in_features=100, out_features=200, bias=True)<br>
)<br>
)<br>
<br>
(The diagram shows an ``nn.<a href="torch.nn.modules.module.html#Module">Module</a>`` ``A``. ``A`` has a nested<br>
submodule ``net_b``, which itself has two submodules ``net_c``<br>
and ``linear``. ``net_c`` then has a submodule ``conv``.)<br>
<br>
To check whether or not we have the ``linear`` submodule, we<br>
would call ``<a href="#SubbandDSP-get_submodule">get_submodule</a>("net_b.linear")``. To check whether<br>
we have the ``conv`` submodule, we would call<br>
``<a href="#SubbandDSP-get_submodule">get_submodule</a>("net_b.net_c.conv")``.<br>
<br>
The runtime of ``get_submodule`` is bounded by the degree<br>
of module nesting in ``target``. A query against<br>
``named_modules`` achieves the same result, but it is O(N) in<br>
the number of transitive modules. So, for a simple check to see<br>
if some submodule exists, ``get_submodule`` should always be<br>
used.<br>
<br>
Args:<br>
target: The fully-qualified string name of the submodule<br>
to look for. (See above example for how to specify a<br>
fully-qualified string.)<br>
<br>
Returns:<br>
torch.nn.<a href="torch.nn.modules.module.html#Module">Module</a>: The submodule referenced by ``target``<br>
<br>
Raises:<br>
AttributeError: If the target string references an invalid<br>
path or resolves to something that is not an<br>
``nn.<a href="torch.nn.modules.module.html#Module">Module</a>``</tt></dd></dl>
<dl><dt><a name="SubbandDSP-half"><strong>half</strong></a>(self: ~T) -> ~T</dt><dd><tt>Casts all floating point parameters and buffers to ``half`` datatype.<br>
<br>
.. note::<br>
This method modifies the module in-place.<br>
<br>
Returns:<br>
<a href="torch.nn.modules.module.html#Module">Module</a>: self</tt></dd></dl>
<dl><dt><a name="SubbandDSP-load_state_dict"><strong>load_state_dict</strong></a>(self, state_dict: 'OrderedDict[str, Tensor]', strict: bool = True)</dt><dd><tt>Copies parameters and buffers from :attr:`state_dict` into<br>
this module and its descendants. If :attr:`strict` is ``True``, then<br>
the keys of :attr:`state_dict` must exactly match the keys returned<br>
by this module's :meth:`~torch.nn.<a href="torch.nn.modules.module.html#Module">Module</a>.state_dict` function.<br>
<br>
Args:<br>
state_dict (dict): a dict containing parameters and<br>
persistent buffers.<br>
strict (bool, optional): whether to strictly enforce that the keys<br>
in :attr:`state_dict` match the keys returned by this module's<br>
:meth:`~torch.nn.<a href="torch.nn.modules.module.html#Module">Module</a>.state_dict` function. Default: ``True``<br>
<br>
Returns:<br>
``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields:<br>
* **missing_keys** is a list of str containing the missing keys<br>
* **unexpected_keys** is a list of str containing the unexpected keys</tt></dd></dl>
<dl><dt><a name="SubbandDSP-modules"><strong>modules</strong></a>(self) -> Iterator[ForwardRef('Module')]</dt><dd><tt>Returns an iterator over all modules in the network.<br>
<br>
Yields:<br>
<a href="torch.nn.modules.module.html#Module">Module</a>: a module in the network<br>
<br>
Note:<br>
Duplicate modules are returned only once. In the following<br>
example, ``l`` will be returned only once.<br>
<br>
Example::<br>
<br>
>>> l = nn.Linear(2, 2)<br>
>>> net = nn.Sequential(l, l)<br>
>>> for idx, m in enumerate(net.<a href="#SubbandDSP-modules">modules</a>()):<br>
print(idx, '->', m)<br>
<br>
0 -> Sequential(<br>
(0): Linear(in_features=2, out_features=2, bias=True)<br>
(1): Linear(in_features=2, out_features=2, bias=True)<br>
)<br>
1 -> Linear(in_features=2, out_features=2, bias=True)</tt></dd></dl>
<dl><dt><a name="SubbandDSP-named_buffers"><strong>named_buffers</strong></a>(self, prefix: str = '', recurse: bool = True) -> Iterator[Tuple[str, torch.Tensor]]</dt><dd><tt>Returns an iterator over module buffers, yielding both the<br>
name of the buffer as well as the buffer itself.<br>
<br>
Args:<br>
prefix (str): prefix to prepend to all buffer names.<br>
recurse (bool): if True, then yields buffers of this module<br>
and all submodules. Otherwise, yields only buffers that<br>
are direct members of this module.<br>
<br>
Yields:<br>
(string, torch.Tensor): Tuple containing the name and buffer<br>
<br>
Example::<br>
<br>
>>> for name, buf in self.<a href="#SubbandDSP-named_buffers">named_buffers</a>():<br>
>>> if name in ['running_var']:<br>
>>> print(buf.size())</tt></dd></dl>
<dl><dt><a name="SubbandDSP-named_children"><strong>named_children</strong></a>(self) -> Iterator[Tuple[str, ForwardRef('Module')]]</dt><dd><tt>Returns an iterator over immediate children modules, yielding both<br>
the name of the module as well as the module itself.<br>
<br>
Yields:<br>
(string, <a href="torch.nn.modules.module.html#Module">Module</a>): Tuple containing a name and child module<br>
<br>
Example::<br>
<br>
>>> for name, module in model.<a href="#SubbandDSP-named_children">named_children</a>():<br>
>>> if name in ['conv4', 'conv5']:<br>
>>> print(module)</tt></dd></dl>
<dl><dt><a name="SubbandDSP-named_modules"><strong>named_modules</strong></a>(self, memo: Union[Set[ForwardRef('Module')], NoneType] = None, prefix: str = '', remove_duplicate: bool = True)</dt><dd><tt>Returns an iterator over all modules in the network, yielding<br>
both the name of the module as well as the module itself.<br>
<br>
Args:<br>
memo: a memo to store the set of modules already added to the result<br>
prefix: a prefix that will be added to the name of the module<br>
remove_duplicate: whether to remove the duplicated module instances in the result<br>
or not<br>
<br>
Yields:<br>
(string, <a href="torch.nn.modules.module.html#Module">Module</a>): Tuple of name and module<br>
<br>
Note:<br>
Duplicate modules are returned only once. In the following<br>
example, ``l`` will be returned only once.<br>
<br>
Example::<br>
<br>
>>> l = nn.Linear(2, 2)<br>
>>> net = nn.Sequential(l, l)<br>
>>> for idx, m in enumerate(net.<a href="#SubbandDSP-named_modules">named_modules</a>()):<br>
print(idx, '->', m)<br>
<br>
0 -> ('', Sequential(<br>
(0): Linear(in_features=2, out_features=2, bias=True)<br>
(1): Linear(in_features=2, out_features=2, bias=True)<br>
))<br>
1 -> ('0', Linear(in_features=2, out_features=2, bias=True))</tt></dd></dl>
<dl><dt><a name="SubbandDSP-named_parameters"><strong>named_parameters</strong></a>(self, prefix: str = '', recurse: bool = True) -> Iterator[Tuple[str, torch.nn.parameter.Parameter]]</dt><dd><tt>Returns an iterator over module parameters, yielding both the<br>
name of the parameter as well as the parameter itself.<br>
<br>
Args:<br>
prefix (str): prefix to prepend to all parameter names.<br>
recurse (bool): if True, then yields parameters of this module<br>
and all submodules. Otherwise, yields only parameters that<br>
are direct members of this module.<br>
<br>
Yields:<br>
(string, Parameter): Tuple containing the name and parameter<br>
<br>
Example::<br>
<br>
>>> for name, param in self.<a href="#SubbandDSP-named_parameters">named_parameters</a>():<br>
>>> if name in ['bias']:<br>
>>> print(param.size())</tt></dd></dl>
<dl><dt><a name="SubbandDSP-parameters"><strong>parameters</strong></a>(self, recurse: bool = True) -> Iterator[torch.nn.parameter.Parameter]</dt><dd><tt>Returns an iterator over module parameters.<br>
<br>
This is typically passed to an optimizer.<br>
<br>
Args:<br>
recurse (bool): if True, then yields parameters of this module<br>
and all submodules. Otherwise, yields only parameters that<br>
are direct members of this module.<br>
<br>
Yields:<br>
Parameter: module parameter<br>
<br>
Example::<br>
<br>
>>> for param in model.<a href="#SubbandDSP-parameters">parameters</a>():<br>
>>> print(<a href="#SubbandDSP-type">type</a>(param), param.size())<br>
<class 'torch.Tensor'> (20L,)<br>
<class 'torch.Tensor'> (20L, 1L, 5L, 5L)</tt></dd></dl>
<dl><dt><a name="SubbandDSP-register_backward_hook"><strong>register_backward_hook</strong></a>(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</dt><dd><tt>Registers a backward hook on the module.<br>
<br>
This function is deprecated in favor of :meth:`nn.<a href="torch.nn.modules.module.html#Module">Module</a>.register_full_backward_hook` and<br>
the behavior of this function will change in future versions.<br>
<br>
Returns:<br>
:class:`torch.utils.hooks.RemovableHandle`:<br>
a handle that can be used to remove the added hook by calling<br>
``handle.remove()``</tt></dd></dl>
<dl><dt><a name="SubbandDSP-register_buffer"><strong>register_buffer</strong></a>(self, name: str, tensor: Union[torch.Tensor, NoneType], persistent: bool = True) -> None</dt><dd><tt>Adds a buffer to the module.<br>
<br>
This is typically used to register a buffer that should not to be<br>
considered a model parameter. For example, BatchNorm's ``running_mean``<br>
is not a parameter, but is part of the module's state. Buffers, by<br>
default, are persistent and will be saved alongside parameters. This<br>
behavior can be changed by setting :attr:`persistent` to ``False``. The<br>
only difference between a persistent buffer and a non-persistent buffer<br>
is that the latter will not be a part of this module's<br>
:attr:`state_dict`.<br>
<br>
Buffers can be accessed as attributes using given names.<br>
<br>
Args:<br>
name (string): name of the buffer. The buffer can be accessed<br>
from this module using the given name<br>
tensor (Tensor): buffer to be registered.<br>
persistent (bool): whether the buffer is part of this module's<br>
:attr:`state_dict`.<br>
<br>
Example::<br>
<br>
>>> self.<a href="#SubbandDSP-register_buffer">register_buffer</a>('running_mean', torch.zeros(num_features))</tt></dd></dl>
<dl><dt><a name="SubbandDSP-register_forward_hook"><strong>register_forward_hook</strong></a>(self, hook: Callable[..., NoneType]) -> torch.utils.hooks.RemovableHandle</dt><dd><tt>Registers a forward hook on the module.<br>
<br>
The hook will be called every time after :func:`forward` has computed an output.<br>
It should have the following signature::<br>
<br>
hook(module, input, output) -> None or modified output<br>
<br>
The input contains only the positional arguments given to the module.<br>
Keyword arguments won't be passed to the hooks and only to the ``forward``.<br>
The hook can modify the output. It can modify the input inplace but<br>
it will not have effect on forward since this is called after<br>
:func:`forward` is called.<br>
<br>
Returns:<br>
:class:`torch.utils.hooks.RemovableHandle`:<br>
a handle that can be used to remove the added hook by calling<br>
``handle.remove()``</tt></dd></dl>
<dl><dt><a name="SubbandDSP-register_forward_pre_hook"><strong>register_forward_pre_hook</strong></a>(self, hook: Callable[..., NoneType]) -> torch.utils.hooks.RemovableHandle</dt><dd><tt>Registers a forward pre-hook on the module.<br>
<br>
The hook will be called every time before :func:`forward` is invoked.<br>
It should have the following signature::<br>
<br>
hook(module, input) -> None or modified input<br>
<br>
The input contains only the positional arguments given to the module.<br>
Keyword arguments won't be passed to the hooks and only to the ``forward``.<br>
The hook can modify the input. User can either return a tuple or a<br>
single modified value in the hook. We will wrap the value into a tuple<br>
if a single value is returned(unless that value is already a tuple).<br>
<br>
Returns:<br>
:class:`torch.utils.hooks.RemovableHandle`:<br>
a handle that can be used to remove the added hook by calling<br>
``handle.remove()``</tt></dd></dl>
<dl><dt><a name="SubbandDSP-register_full_backward_hook"><strong>register_full_backward_hook</strong></a>(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</dt><dd><tt>Registers a backward hook on the module.<br>
<br>
The hook will be called every time the gradients with respect to module<br>
inputs are computed. The hook should have the following signature::<br>
<br>
hook(module, grad_input, grad_output) -> tuple(Tensor) or None<br>
<br>
The :attr:`grad_input` and :attr:`grad_output` are tuples that contain the gradients<br>
with respect to the inputs and outputs respectively. The hook should<br>
not modify its arguments, but it can optionally return a new gradient with<br>
respect to the input that will be used in place of :attr:`grad_input` in<br>
subsequent computations. :attr:`grad_input` will only correspond to the inputs given<br>
as positional arguments and all kwarg arguments are ignored. Entries<br>
in :attr:`grad_input` and :attr:`grad_output` will be ``None`` for all non-Tensor<br>
arguments.<br>
<br>
.. warning ::<br>
Modifying inputs or outputs inplace is not allowed when using backward hooks and<br>
will raise an error.<br>
<br>
Returns:<br>
:class:`torch.utils.hooks.RemovableHandle`:<br>
a handle that can be used to remove the added hook by calling<br>
``handle.remove()``</tt></dd></dl>
<dl><dt><a name="SubbandDSP-register_parameter"><strong>register_parameter</strong></a>(self, name: str, param: Union[torch.nn.parameter.Parameter, NoneType]) -> None</dt><dd><tt>Adds a parameter to the module.<br>
<br>
The parameter can be accessed as an attribute using given name.<br>
<br>
Args:<br>
name (string): name of the parameter. The parameter can be accessed<br>
from this module using the given name<br>
param (Parameter): parameter to be added to the module.</tt></dd></dl>
<dl><dt><a name="SubbandDSP-requires_grad_"><strong>requires_grad_</strong></a>(self: ~T, requires_grad: bool = True) -> ~T</dt><dd><tt>Change if autograd should record operations on parameters in this<br>
module.<br>
<br>
This method sets the parameters' :attr:`requires_grad` attributes<br>
in-place.<br>
<br>
This method is helpful for freezing part of the module for finetuning<br>
or training parts of a model individually (e.g., GAN training).<br>
<br>
See :ref:`locally-disable-grad-doc` for a comparison between<br>
`.<a href="#SubbandDSP-requires_grad_">requires_grad_</a>()` and several similar mechanisms that may be confused with it.<br>
<br>
Args:<br>
requires_grad (bool): whether autograd should record operations on<br>
parameters in this module. Default: ``True``.<br>
<br>
Returns:<br>
<a href="torch.nn.modules.module.html#Module">Module</a>: self</tt></dd></dl>
<dl><dt><a name="SubbandDSP-share_memory"><strong>share_memory</strong></a>(self: ~T) -> ~T</dt><dd><tt>See :meth:`torch.Tensor.share_memory_`</tt></dd></dl>
<dl><dt><a name="SubbandDSP-state_dict"><strong>state_dict</strong></a>(self, destination=None, prefix='', keep_vars=False)</dt><dd><tt>Returns a dictionary containing a whole state of the module.<br>
<br>
Both parameters and persistent buffers (e.g. running averages) are<br>
included. Keys are corresponding parameter and buffer names.<br>
<br>
Returns:<br>
dict:<br>
a dictionary containing a whole state of the module<br>
<br>
Example::<br>
<br>
>>> module.<a href="#SubbandDSP-state_dict">state_dict</a>().keys()<br>
['bias', 'weight']</tt></dd></dl>
<dl><dt><a name="SubbandDSP-to"><strong>to</strong></a>(self, *args, **kwargs)</dt><dd><tt>Moves and/or casts the parameters and buffers.<br>
<br>
This can be called as<br>
<br>
.. function:: <a href="#SubbandDSP-to">to</a>(device=None, dtype=None, non_blocking=False)<br>
<br>
.. function:: <a href="#SubbandDSP-to">to</a>(dtype, non_blocking=False)<br>
<br>
.. function:: <a href="#SubbandDSP-to">to</a>(tensor, non_blocking=False)<br>
<br>
.. function:: <a href="#SubbandDSP-to">to</a>(memory_format=torch.channels_last)<br>
<br>
Its signature is similar to :meth:`torch.Tensor.to`, but only accepts<br>
floating point or complex :attr:`dtype`s. In addition, this method will<br>
only cast the floating point or complex parameters and buffers to :attr:`dtype`<br>
(if given). The integral parameters and buffers will be moved<br>
:attr:`device`, if that is given, but with dtypes unchanged. When<br>
:attr:`non_blocking` is set, it tries to convert/move asynchronously<br>
with respect to the host if possible, e.g., moving CPU Tensors with<br>
pinned memory to CUDA devices.<br>
<br>
See below for examples.<br>
<br>
.. note::<br>
This method modifies the module in-place.<br>
<br>
Args:<br>
device (:class:`torch.device`): the desired device of the parameters<br>
and buffers in this module<br>
dtype (:class:`torch.dtype`): the desired floating point or complex dtype of<br>
the parameters and buffers in this module<br>
tensor (torch.Tensor): Tensor whose dtype and device are the desired<br>
dtype and device for all parameters and buffers in this module<br>
memory_format (:class:`torch.memory_format`): the desired memory<br>
format for 4D parameters and buffers in this module (keyword<br>
only argument)<br>
<br>
Returns:<br>
<a href="torch.nn.modules.module.html#Module">Module</a>: self<br>
<br>
Examples::<br>
<br>
>>> linear = nn.Linear(2, 2)<br>
>>> linear.weight<br>
Parameter containing:<br>
tensor([[ 0.1913, -0.3420],<br>
[-0.5113, -0.2325]])<br>
>>> linear.<a href="#SubbandDSP-to">to</a>(torch.double)<br>
Linear(in_features=2, out_features=2, bias=True)<br>
>>> linear.weight<br>
Parameter containing:<br>
tensor([[ 0.1913, -0.3420],<br>
[-0.5113, -0.2325]], dtype=torch.float64)<br>
>>> gpu1 = torch.device("cuda:1")<br>
>>> linear.<a href="#SubbandDSP-to">to</a>(gpu1, dtype=torch.half, non_blocking=True)<br>
Linear(in_features=2, out_features=2, bias=True)<br>
>>> linear.weight<br>
Parameter containing:<br>
tensor([[ 0.1914, -0.3420],<br>
[-0.5112, -0.2324]], dtype=torch.float16, device='cuda:1')<br>
>>> cpu = torch.device("cpu")<br>
>>> linear.<a href="#SubbandDSP-to">to</a>(cpu)<br>
Linear(in_features=2, out_features=2, bias=True)<br>
>>> linear.weight<br>
Parameter containing:<br>
tensor([[ 0.1914, -0.3420],<br>
[-0.5112, -0.2324]], dtype=torch.float16)<br>
<br>
>>> linear = nn.Linear(2, 2, bias=None).<a href="#SubbandDSP-to">to</a>(torch.cdouble)<br>
>>> linear.weight<br>
Parameter containing:<br>
tensor([[ 0.3741+0.j, 0.2382+0.j],<br>
[ 0.5593+0.j, -0.4443+0.j]], dtype=torch.complex128)<br>
>>> linear(torch.ones(3, 2, dtype=torch.cdouble))<br>
tensor([[0.6122+0.j, 0.1150+0.j],<br>
[0.6122+0.j, 0.1150+0.j],<br>
[0.6122+0.j, 0.1150+0.j]], dtype=torch.complex128)</tt></dd></dl>
<dl><dt><a name="SubbandDSP-to_empty"><strong>to_empty</strong></a>(self: ~T, *, device: Union[str, torch.device]) -> ~T</dt><dd><tt>Moves the parameters and buffers to the specified device without copying storage.<br>
<br>
Args:<br>
device (:class:`torch.device`): The desired device of the parameters<br>
and buffers in this module.<br>
<br>
Returns:<br>
<a href="torch.nn.modules.module.html#Module">Module</a>: self</tt></dd></dl>
<dl><dt><a name="SubbandDSP-train"><strong>train</strong></a>(self: ~T, mode: bool = True) -> ~T</dt><dd><tt>Sets the module in training mode.<br>
<br>
This has any effect only on certain modules. See documentations of<br>
particular modules for details of their behaviors in training/evaluation<br>
mode, if they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`,<br>
etc.<br>
<br>
Args:<br>
mode (bool): whether to set training mode (``True``) or evaluation<br>
mode (``False``). Default: ``True``.<br>
<br>
Returns:<br>
<a href="torch.nn.modules.module.html#Module">Module</a>: self</tt></dd></dl>
<dl><dt><a name="SubbandDSP-type"><strong>type</strong></a>(self: ~T, dst_type: Union[torch.dtype, str]) -> ~T</dt><dd><tt>Casts all parameters and buffers to :attr:`dst_type`.<br>
<br>
.. note::<br>
This method modifies the module in-place.<br>
<br>
Args:<br>
dst_type (type or string): the desired type<br>
<br>
Returns:<br>
<a href="torch.nn.modules.module.html#Module">Module</a>: self</tt></dd></dl>
<dl><dt><a name="SubbandDSP-xpu"><strong>xpu</strong></a>(self: ~T, device: Union[int, torch.device, NoneType] = None) -> ~T</dt><dd><tt>Moves all model parameters and buffers to the XPU.<br>
<br>
This also makes associated parameters and buffers different objects. So<br>
it should be called before constructing optimizer if the module will<br>
live on XPU while being optimized.<br>
<br>
.. note::<br>
This method modifies the module in-place.<br>
<br>
Arguments:<br>
device (int, optional): if specified, all parameters will be<br>
copied to that device<br>
<br>
Returns:<br>
<a href="torch.nn.modules.module.html#Module">Module</a>: self</tt></dd></dl>
<dl><dt><a name="SubbandDSP-zero_grad"><strong>zero_grad</strong></a>(self, set_to_none: bool = False) -> None</dt><dd><tt>Sets gradients of all model parameters to zero. See similar function<br>
under :class:`torch.optim.Optimizer` for more context.<br>
<br>
Args:<br>
set_to_none (bool): instead of setting to zero, set the grads to None.<br>
See :meth:`torch.optim.Optimizer.zero_grad` for details.</tt></dd></dl>
<hr>
Data descriptors inherited from <a href="torch.nn.modules.module.html#Module">torch.nn.modules.module.Module</a>:<br>
<dl><dt><strong>__dict__</strong></dt>
<dd><tt>dictionary for instance variables (if defined)</tt></dd>
</dl>
<dl><dt><strong>__weakref__</strong></dt>
<dd><tt>list of weak references to the object (if defined)</tt></dd>
</dl>
<hr>
Data and other attributes inherited from <a href="torch.nn.modules.module.html#Module">torch.nn.modules.module.Module</a>:<br>
<dl><dt><strong>T_destination</strong> = ~T_destination</dl>
<dl><dt><strong>__annotations__</strong> = {'__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'>}</dl>
<dl><dt><strong>dump_patches</strong> = False</dl>
</td></tr></table></td></tr></table><p>
<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="section">
<tr bgcolor="#eeaa77">
<td colspan=3 valign=bottom> <br>
<font color="#ffffff" face="helvetica, arial"><big><strong>Functions</strong></big></font></td></tr>
<tr><td bgcolor="#eeaa77"><tt> </tt></td><td> </td>
<td width="100%"><dl><dt><a name="-ceil"><strong>ceil</strong></a>(x, /)</dt><dd><tt>Return the ceiling of x as an Integral.<br>
<br>
This is the smallest integer >= x.</tt></dd></dl>
<dl><dt><a name="-test"><strong>test</strong></a>()</dt></dl>
</td></tr></table>
</body></html>