/usr/local/lib64/python3.6/site-packages/torch/nn/modules/__pycache__
Edit: /usr/local/lib64/python3.6/site-packages/torch/nn/modules/__pycache__/linear.cpython-36.pyc (10188B)
3
Ege( @ s d dl Z d dlZd dlmZ d dlmZmZ ddlmZ ddlm Z ddl
m Z dd l m
Z
G d
d d e ZG d d
d
e ZG dd deZG dd de ZG dd de
eZdS ) N)Tensor) ParameterUninitializedParameter )
functional)init )Module)LazyModuleMixinc s0 e Zd ZdZ fddZeedddZ ZS )Identitya A placeholder identity operator that is argument-insensitive.
Args:
args: any argument (unused)
kwargs: any keyword argument (unused)
Shape:
- Input: :math:`(*)`, where :math:`*` means any number of dimensions.
- Output: :math:`(*)`, same shape as the input.
Examples::
>>> m = nn.Identity(54, unused_argument1=0.1, unused_argument2=False)
>>> input = torch.randn(128, 20)
>>> output = m(input)
>>> print(output.size())
torch.Size([128, 20])
c s t t| j d S )N)superr __init__)selfargskwargs) __class__ C/usr/local/lib64/python3.6/site-packages/torch/nn/modules/linear.pyr
s zIdentity.__init__)inputreturnc C s |S )Nr )r r r r r forward# s zIdentity.forward)__name__
__module__ __qualname____doc__r
r r
__classcell__r r )r r r s r c sp e Zd ZU dZddgZeee deee
dd fdd
Z dd d
d Z eed d
dZ
ed ddZ ZS )Lineara Applies a linear transformation to the incoming data: :math:`y = xA^T + b`
This module supports :ref:`TensorFloat32
`.
Args:
in_features: size of each input sample
out_features: size of each output sample
bias: If set to ``False``, the layer will not learn an additive bias.
Default: ``True``
Shape:
- Input: :math:`(*, H_{in})` where :math:`*` means any number of
dimensions including none and :math:`H_{in} = \text{in\_features}`.
- Output: :math:`(*, H_{out})` where all but the last dimension
are the same shape as the input and :math:`H_{out} = \text{out\_features}`.
Attributes:
weight: the learnable weights of the module of shape
:math:`(\text{out\_features}, \text{in\_features})`. The values are
initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where
:math:`k = \frac{1}{\text{in\_features}}`
bias: the learnable bias of the module of shape :math:`(\text{out\_features})`.
If :attr:`bias` is ``True``, the values are initialized from
:math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
:math:`k = \frac{1}{\text{in\_features}}`
Examples::
>>> m = nn.Linear(20, 30)
>>> input = torch.randn(128, 20)
>>> output = m(input)
>>> print(output.size())
torch.Size([128, 30])
in_features out_featuresTN)r r biasr c sn ||d}t t| j || _|| _ttj||ff|| _|rVttj|f|| _ n | j
dd | j d S )N)devicedtyper ) r r r
r r r torchemptyweightr register_parameterreset_parameters)r r r r r r! factory_kwargs)r r r r
O s
zLinear.__init__)r c C s` t j| jtjdd | jd k r\t j| j\}}|dkrFdtj| nd}t j| j| | d S )N )ar r )r Zkaiming_uniform_r$ mathsqrtr Z _calculate_fan_in_and_fan_outuniform_)r Zfan_in_boundr r r r& \ s
zLinear.reset_parameters)r r c C s t j|| j| jS )N)FZlinearr$ r )r r r r r r f s zLinear.forwardc C s dj | j| j| jd k S )Nz(in_features={}, out_features={}, bias={})formatr r r )r r r r
extra_repri s zLinear.extra_repr)TNN)r r r r
__constants__intr r r r$ boolr
r& r strr1 r r r )r r r ' s
"
r c s* e Zd Zdeeedd fdd
Z ZS ) NonDynamicallyQuantizableLinearTN)r r r r c s t j|||||d d S )N)r r r! )r r
)r r r r r r! )r r r r
u s z(NonDynamicallyQuantizableLinear.__init__)TNN)r r r r3 r4 r
r r r )r r r6 t s r6 c sz e Zd ZU dZdddgZeeee
deeee dd fdd
Z dd
d d Z
e e e d
ddZed
ddZ ZS )Bilineara Applies a bilinear transformation to the incoming data:
:math:`y = x_1^T A x_2 + b`
Args:
in1_features: size of each first input sample
in2_features: size of each second input sample
out_features: size of each output sample
bias: If set to False, the layer will not learn an additive bias.
Default: ``True``
Shape:
- Input1: :math:`(N, *, H_{in1})` where :math:`H_{in1}=\text{in1\_features}` and
:math:`*` means any number of additional dimensions. All but the last dimension
of the inputs should be the same.
- Input2: :math:`(N, *, H_{in2})` where :math:`H_{in2}=\text{in2\_features}`.
- Output: :math:`(N, *, H_{out})` where :math:`H_{out}=\text{out\_features}`
and all but the last dimension are the same shape as the input.
Attributes:
weight: the learnable weights of the module of shape
:math:`(\text{out\_features}, \text{in1\_features}, \text{in2\_features})`.
The values are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where
:math:`k = \frac{1}{\text{in1\_features}}`
bias: the learnable bias of the module of shape :math:`(\text{out\_features})`.
If :attr:`bias` is ``True``, the values are initialized from
:math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where
:math:`k = \frac{1}{\text{in1\_features}}`
Examples::
>>> m = nn.Bilinear(20, 30, 40)
>>> input1 = torch.randn(128, 20)
>>> input2 = torch.randn(128, 30)
>>> output = m(input1, input2)
>>> print(output.size())
torch.Size([128, 40])
in1_features in2_featuresr TN)r8 r9 r r r c sv ||d}t t| j || _|| _|| _ttj|||ff|| _ |r^ttj|f|| _
n | j dd | j d S )N)r r! r )
r r7 r
r8 r9 r r r" r# r$ r r% r& )r r8 r9 r r r r! r' )r r r r
s
zBilinear.__init__)r c C sH dt j| jjd }tj| j| | | jd k rDtj| j| | d S )Nr )r* r+ r$ sizer r, r )r r. r r r r& s
zBilinear.reset_parameters)input1input2r c C s t j||| j| jS )N)r/ Zbilinearr$ r )r r; r< r r r r s zBilinear.forwardc C s dj | j| j| j| jd k S )Nz:in1_features={}, in2_features={}, out_features={}, bias={})r0 r8 r9 r r )r r r r r1 s zBilinear.extra_repr)TNN)r r r r r2 r3 r8 r9 r r r$ r4 r
r& r r5 r1 r r r )r r r7 { s
%
r7 c sZ e Zd ZU dZeZeed e e
dd fdd
Z dd fdd Z ddd
d Z
ZS )
LazyLineara A :class:`torch.nn.Linear` module where `in_features` is inferred.
In this module, the `weight` and `bias` are of :class:`torch.nn.UninitializedParameter`
class. They will be initialized after the first call to ``forward`` is done and the
module will become a regular :class:`torch.nn.Linear` module. The ``in_features`` argument
of the :class:`Linear` is inferred from the ``input.shape[-1]``.
Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation
on lazy modules and their limitations.
Args:
out_features: size of each output sample
bias: If set to ``False``, the layer will not learn an additive bias.
Default: ``True``
Attributes:
weight: the learnable weights of the module of shape
:math:`(\text{out\_features}, \text{in\_features})`. The values are
initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where
:math:`k = \frac{1}{\text{in\_features}}`
bias: the learnable bias of the module of shape :math:`(\text{out\_features})`.
If :attr:`bias` is ``True``, the values are initialized from
:math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
:math:`k = \frac{1}{\text{in\_features}}`
TN)r r r c s@ ||d}t jddd tf || _|| _|r r" Zno_gradshaper r$ Z materializer r r& )r r r r r initialize_parameters s
z LazyLinear.initialize_parameters)TNN)r r r r r Z
cls_to_becomer r$ r r3 r4 r
r& rA r r r )r r r= s
r= )r* r" r Ztorch.nn.parameterr r r r/ r moduler Zlazyr
r r r6 r7 r= r r r r s MJ