elektronn3.modules.wsconv module¶
Weight Standartized convolution layers, see https://arxiv.org/abs/1903.10520
- class elektronn3.modules.wsconv.FWS(layer, learnable_gain=True, const_eval=False, eps=0.0001)[source]¶
Bases:
torch.nn.
Kind of like weight standardization, but changes weights in place
- class elektronn3.modules.wsconv.WSConv1d(*args: Any, **kwargs: Any)[source]¶
Bases:
torch.nn.
Applies a 1D convolution over an input signal composed of several input planes. In the simplest case, the output value of the layer with input size and output can be precisely described as: .. math:
\text{out}(N_i, C_{\text{out}_j}) = \text{bias}(C_{\text{out}_j}) + \sum_{k = 0}^{C_{in} - 1} \text{weight}(C_{\text{out}_j}, k) \star \text{input}(N_i, k)
where is the valid cross-correlation operator, is a batch size, denotes a number of channels, is a length of signal sequence. This module supports TensorFloat32. *
stride
controls the stride for the cross-correlation, a singlenumber or a one-element tuple.
padding
controls the amount of implicit zero-paddings on both sides forpadding
number of points.dilation
controls the spacing between the kernel points; also known as the à trous algorithm. It is harder to describe, but this link has a nice visualization of whatdilation
does.groups
controls the connections between inputs and outputs.in_channels
andout_channels
must both be divisible bygroups
. For example,At groups=1, all inputs are convolved to all outputs.
At groups=2, the operation becomes equivalent to having two conv layers side by side, each seeing half the input channels, and producing half the output channels, and both subsequently concatenated.
At groups=
in_channels
, each input channel is convolved with its own set of filters, of size .
Note
Depending of the size of your kernel, several (of the last) columns of the input might be lost, because it is a valid cross-correlation, and not a full cross-correlation. It is up to the user to add proper padding.
Note
When groups == in_channels and out_channels == K * in_channels, where K is a positive integer, this operation is also termed in literature as depthwise convolution. In other words, for an input of size , a depthwise convolution with a depthwise multiplier K, can be constructed by arguments .
Note
In some circumstances when using the CUDA backend with CuDNN, this operator may select a nondeterministic algorithm to increase performance. If this is undesirable, you can try to make the operation deterministic (potentially at a performance cost) by setting
torch.backends.cudnn.deterministic = True
. Please see the notes on /notes/randomness for background.- Parameters
in_channels (int) – Number of channels in the input image
out_channels (int) – Number of channels produced by the convolution
kernel_size (int or tuple) – Size of the convolving kernel
stride (int or tuple, optional) – Stride of the convolution. Default: 1
padding (int or tuple, optional) – Zero-padding added to both sides of the input. Default: 0
padding_mode (string, optional) –
'zeros'
,'reflect'
,'replicate'
or'circular'
. Default:'zeros'
dilation (int or tuple, optional) – Spacing between kernel elements. Default: 1
groups (int, optional) – Number of blocked connections from input channels to output channels. Default: 1
bias (bool, optional) – If
True
, adds a learnable bias to the output. Default:True
- Shape:
Input:
Output: where .. math:
L_{out} = \left\lfloor\frac{L_{in} + 2 \times \text{padding} - \text{dilation} \times (\text{kernel\_size} - 1) - 1}{\text{stride}} + 1\right\rfloor
- weight¶
the learnable weights of the module of shape . The values of these weights are sampled from where
- Type
Tensor
- bias¶
the learnable bias of the module of shape (out_channels). If
bias
isTrue
, then the values of these weights are sampled from where- Type
Tensor
- Examples::
>>> m = nn.Conv1d(16, 33, 3, stride=2) >>> input = torch.randn(20, 16, 50) >>> output = m(input)
- class elektronn3.modules.wsconv.WSConv2d(*args: Any, **kwargs: Any)[source]¶
Bases:
torch.nn.
- Applies a 2D convolution over an input signal composed of several input
planes after weight normalization/standardization. Reference: https://github.com/deepmind/deepmind-research/blob/master/nfnets/base.py#L121 In the simplest case, the output value of the layer with input size and output can be precisely described as: .. math:
ext{out}(N_i, C_{ ext{out}_j}) = ext{bias}(C_{ ext{out}_j}) + \sum_{k = 0}^{C_{ ext{in}} - 1} ext{weight}(C_{ ext{out}_j}, k) \star ext{input}(N_i, k)
where is the valid 2D cross-correlation operator, is a batch size, denotes a number of channels, is a height of input planes in pixels, and is width in pixels. This module supports TensorFloat32. *
stride
controls the stride for the cross-correlation, a singlenumber or a tuple.
padding
controls the amount of implicit zero-paddings on both sides forpadding
number of points for each dimension.dilation
controls the spacing between the kernel points; also known as the à trous algorithm. It is harder to describe, but this link has a nice visualization of whatdilation
does.groups
controls the connections between inputs and outputs.in_channels
andout_channels
must both be divisible bygroups
. For example,At groups=1, all inputs are convolved to all outputs.
At groups=2, the operation becomes equivalent to having two conv layers side by side, each seeing half the input channels, and producing half the output channels, and both subsequently concatenated.
At groups=
in_channels
, each input channel is convolved with its own set of filters, of size: :math:`leftlfloor
rac{out_channels}{in_channels} ight floor`.
- The parameters
kernel_size
,stride
,padding
,dilation
can either be: a single
int
– in which case the same value is used for the height and width dimensiona
tuple
of two ints – in which case, the first int is used for the height dimension, and the second int for the width dimension
- Note:
Depending of the size of your kernel, several (of the last) columns of the input might be lost, because it is a valid cross-correlation, and not a full cross-correlation. It is up to the user to add proper padding.
- Note:
When groups == in_channels and out_channels == K * in_channels, where K is a positive integer, this operation is also termed in literature as depthwise convolution. In other words, for an input of size , a depthwise convolution with a depthwise multiplier K, can be constructed by arguments .
- Note:
In some circumstances when using the CUDA backend with CuDNN, this operator may select a nondeterministic algorithm to increase performance. If this is undesirable, you can try to make the operation deterministic (potentially at a performance cost) by setting
torch.backends.cudnn.deterministic = True
. Please see the notes on /notes/randomness for background.- Args:
in_channels (int): Number of channels in the input image out_channels (int): Number of channels produced by the convolution kernel_size (int or tuple): Size of the convolving kernel stride (int or tuple, optional): Stride of the convolution. Default: 1 padding (int or tuple, optional): Zero-padding added to both sides of
the input. Default: 0
- padding_mode (string, optional):
'zeros'
,'reflect'
, 'replicate'
or'circular'
. Default:'zeros'
dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 groups (int, optional): Number of blocked connections from input
channels to output channels. Default: 1
- bias (bool, optional): If
True
, adds a learnable bias to the output. Default:
True
- padding_mode (string, optional):
- Shape:
Input:
Output: where .. math:
H_{out} = \left\lfloor
- rac{H_{in} + 2 imes ext{padding}[0] - ext{dilation}[0]
imes ( ext{kernel_size}[0] - 1) - 1}{ ext{stride}[0]} + 1
ight floor
- rac{W_{in} + 2 imes ext{padding}[1] - ext{dilation}[1]
imes ( ext{kernel_size}[1] - 1) - 1}{ ext{stride}[1]} + 1
ight floor
- Attributes:
- weight (Tensor): the learnable weights of the module of shape
:math:`( ext{out_channels},
- rac{ ext{in_channels}}{ ext{groups}},`
:math:` ext{kernel_size[0]}, ext{kernel_size[1]})`. The values of these weights are sampled from where :math:`k =
- rac{groups}{C_ ext{in} * prod_{i=0}^{1} ext{kernel_size}[i]}`
- bias (Tensor): the learnable bias of the module of shape
(out_channels). If
bias
isTrue
, then the values of these weights are sampled from where :math:`k =
- rac{groups}{C_ ext{in} * prod_{i=0}^{1} ext{kernel_size}[i]}`
- Examples:
>>> # With square kernels and equal stride >>> m = WSConv2d(16, 33, 3, stride=2) >>> # non-square kernels and unequal stride and with padding >>> m = WSConv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2)) >>> # non-square kernels and unequal stride and with padding and dilation >>> m = WSConv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2), dilation=(3, 1)) >>> input = torch.randn(20, 16, 50, 100) >>> output = m(input)
- class elektronn3.modules.wsconv.WSConvTranspose2d(in_channels, out_channels, kernel_size, stride=1, padding=0, output_padding=0, groups=1, bias=True, dilation=1, padding_mode='zeros')[source]¶
Bases:
torch.nn.
- Applies a 2D transposed convolution operator over an input image
composed of several input planes after weight normalization/standardization. This module can be seen as the gradient of Conv2d with respect to its input. It is also known as a fractionally-strided convolution or a deconvolution (although it is not an actual deconvolution operation). This module supports TensorFloat32. *
stride
controls the stride for the cross-correlation. *padding
controls the amount of implicit zero-paddings on bothsides for
dilation * (kernel_size - 1) - padding
number of points. See note below for details.output_padding
controls the additional size added to one side of the output shape. See note below for details.dilation
controls the spacing between the kernel points; also known as the à trous algorithm. It is harder to describe, but this link has a nice visualization of whatdilation
does.groups
controls the connections between inputs and outputs.in_channels
andout_channels
must both be divisible bygroups
. For example,At groups=1, all inputs are convolved to all outputs.
At groups=2, the operation becomes equivalent to having two conv layers side by side, each seeing half the input channels, and producing half the output channels, and both subsequently concatenated.
At groups=
in_channels
, each input channel is convolved with its own set of filters (of size :math:`leftlfloor
rac{out_channels}{in_channels} ight floor`).
The parameters
kernel_size
,stride
,padding
,output_padding
can either be:a single
int
– in which case the same value is used for the height and width dimensionsa
tuple
of two ints – in which case, the first int is used for the height dimension, and the second int for the width dimension
Note
Depending of the size of your kernel, several (of the last) columns of the input might be lost, because it is a valid cross-correlation, and not a full cross-correlation. It is up to the user to add proper padding.
- Note:
The
padding
argument effectively addsdilation * (kernel_size - 1) - padding
amount of zero padding to both sizes of the input. This is set so that when aConv2d
and aConvTranspose2d
are initialized with same parameters, they are inverses of each other in regard to the input and output shapes. However, whenstride > 1
,Conv2d
maps multiple input shapes to the same output shape.output_padding
is provided to resolve this ambiguity by effectively increasing the calculated output shape on one side. Note thatoutput_padding
is only used to find output shape, but does not actually add zero-padding to output.- Note:
In some circumstances when using the CUDA backend with CuDNN, this operator may select a nondeterministic algorithm to increase performance. If this is undesirable, you can try to make the operation deterministic (potentially at a performance cost) by setting
torch.backends.cudnn.deterministic = True
. Please see the notes on /notes/randomness for background.- Args:
in_channels (int): Number of channels in the input image out_channels (int): Number of channels produced by the convolution kernel_size (int or tuple): Size of the convolving kernel stride (int or tuple, optional): Stride of the convolution. Default: 1 padding (int or tuple, optional):
dilation * (kernel_size - 1) - padding
zero-paddingwill be added to both sides of each dimension in the input. Default: 0
- output_padding (int or tuple, optional): Additional size added to one side
of each dimension in the output shape. Default: 0
groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 bias (bool, optional): If
True
, adds a learnable bias to the output. Default:True
dilation (int or tuple, optional): Spacing between kernel elements. Default: 1- Shape:
Input:
Output: where
- Attributes:
- weight (Tensor): the learnable weights of the module of shape
:math:`( ext{in_channels},
- rac{ ext{out_channels}}{ ext{groups}},`
:math:` ext{kernel_size[0]}, ext{kernel_size[1]})`. The values of these weights are sampled from where :math:`k =
- rac{groups}{C_ ext{out} * prod_{i=0}^{1} ext{kernel_size}[i]}`
- bias (Tensor): the learnable bias of the module of shape (out_channels)
If
bias
isTrue
, then the values of these weights are sampled from where :math:`k =
- rac{groups}{C_ ext{out} * prod_{i=0}^{1} ext{kernel_size}[i]}`
- Examples::
>>> # With square kernels and equal stride >>> m = WSConvTranspose2d(16, 33, 3, stride=2) >>> # non-square kernels and unequal stride and with padding >>> m = WSConvTranspose2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2)) >>> input = torch.randn(20, 16, 50, 100) >>> output = m(input) >>> # exact output size can be also specified as an argument >>> input = torch.randn(1, 16, 12, 12) >>> downsample = WSConv2d(16, 16, 3, stride=2, padding=1) >>> upsample = WSConvTranspose2d(16, 16, 3, stride=2, padding=1) >>> h = downsample(input) >>> h.size() torch.Size([1, 16, 6, 6]) >>> output = upsample(h, output_size=input.size()) >>> output.size() torch.Size([1, 16, 12, 12])