# Source code for torch.nn.modules.conv

```
# coding=utf-8
import math
import torch
from torch.nn.parameter import Parameter
from .. import functional as F
from .module import Module
from .utils import _single, _pair, _triple
class _ConvNd(Module):
def __init__(self, in_channels, out_channels, kernel_size, stride,
padding, dilation, transposed, output_padding, groups, bias):
super(_ConvNd, self).__init__()
if in_channels % groups != 0:
raise ValueError('in_channels must be divisible by groups')
if out_channels % groups != 0:
raise ValueError('out_channels must be divisible by groups')
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.dilation = dilation
self.transposed = transposed
self.output_padding = output_padding
self.groups = groups
if transposed:
self.weight = Parameter(torch.Tensor(
in_channels, out_channels // groups, *kernel_size))
else:
self.weight = Parameter(torch.Tensor(
out_channels, in_channels // groups, *kernel_size))
if bias:
self.bias = Parameter(torch.Tensor(out_channels))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self):
n = self.in_channels
for k in self.kernel_size:
n *= k
stdv = 1. / math.sqrt(n)
self.weight.data.uniform_(-stdv, stdv)
if self.bias is not None:
self.bias.data.uniform_(-stdv, stdv)
def __repr__(self):
s = ('{name}({in_channels}, {out_channels}, kernel_size={kernel_size}'
', stride={stride}')
if self.padding != (0,) * len(self.padding):
s += ', padding={padding}'
if self.dilation != (1,) * len(self.dilation):
s += ', dilation={dilation}'
if self.output_padding != (0,) * len(self.output_padding):
s += ', output_padding={output_padding}'
if self.groups != 1:
s += ', groups={groups}'
if self.bias is None:
s += ', bias=False'
s += ')'
return s.format(name=self.__class__.__name__, **self.__dict__)
[docs]class Conv1d(_ConvNd):
r"""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
:math:`(N, C_{in}, L)` and output :math:`(N, C_{out}, L_{out})` can be
precisely described as:
.. math::
\begin{array}{ll}
out(N_i, C_{out_j}) = bias(C_{out_j})
+ \sum_{{k}=0}^{C_{in}-1} weight(C_{out_j}, k) \star input(N_i, k)
\end{array}
where :math:`\star` is the valid `cross-correlation`_ operator,
:math:`N` is a batch size, :math:`C` denotes a number of channels,
:math:`L` is a length of signal sequence.
| :attr:`stride` controls the stride for the cross-correlation, a single
number or a one-element tuple.
| :attr:`padding` controls the amount of implicit zero-paddings on both
| sides for :attr:`padding` number of points.
| :attr:`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 what :attr:`dilation` does.
| :attr:`groups` controls the connections between inputs and outputs.
`in_channels` and `out_channels` must both be divisible by `groups`.
| 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 `out_channels // in_channels`).
.. 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 configuration when `groups == in_channels` and `out_channels = K * in_channels`
where `K` is a positive integer is termed in literature as depthwise convolution.
In other words, for an input of size :math:`(N, C_{in}, L_{in})`, if you want a
depthwise convolution with a depthwise multiplier `K`,
then you use the constructor arguments
:math:`(in\_channels=C_{in}, out\_channels=C_{in} * K, ..., groups=C_{in})`
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
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: :math:`(N, C_{in}, L_{in})`
- Output: :math:`(N, C_{out}, L_{out})` where
:math:`L_{out} = floor((L_{in} + 2 * padding - dilation * (kernel\_size - 1) - 1) / stride + 1)`
Attributes:
weight (Tensor): the learnable weights of the module of shape
(out_channels, in_channels, kernel_size)
bias (Tensor): the learnable bias of the module of shape
(out_channels)
Examples::
>>> m = nn.Conv1d(16, 33, 3, stride=2)
>>> input = autograd.Variable(torch.randn(20, 16, 50))
>>> output = m(input)
.. _cross-correlation:
https://en.wikipedia.org/wiki/Cross-correlation
.. _link:
https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md
"""
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True):
kernel_size = _single(kernel_size)
stride = _single(stride)
padding = _single(padding)
dilation = _single(dilation)
super(Conv1d, self).__init__(
in_channels, out_channels, kernel_size, stride, padding, dilation,
False, _single(0), groups, bias)
def forward(self, input):
return F.conv1d(input, self.weight, self.bias, self.stride,
self.padding, self.dilation, self.groups)
[docs]class Conv2d(_ConvNd):
r"""Applies a 2D convolution over an input signal composed of several input
planes.
In the simplest case, the output value of the layer with input size
:math:`(N, C_{in}, H, W)` and output :math:`(N, C_{out}, H_{out}, W_{out})`
can be precisely described as:
.. math::
\begin{array}{ll}
out(N_i, C_{out_j}) = bias(C_{out_j})
+ \sum_{{k}=0}^{C_{in}-1} weight(C_{out_j}, k) \star input(N_i, k)
\end{array}
where :math:`\star` is the valid 2D `cross-correlation`_ operator,
:math:`N` is a batch size, :math:`C` denotes a number of channels,
:math:`H` is a height of input planes in pixels, and :math:`W` is
width in pixels.
| :attr:`stride` controls the stride for the cross-correlation, a single
number or a tuple.
| :attr:`padding` controls the amount of implicit zero-paddings on both
| sides for :attr:`padding` number of points for each dimension.
| :attr:`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 what :attr:`dilation` does.
| :attr:`groups` controls the connections between inputs and outputs.
`in_channels` and `out_channels` must both be divisible by `groups`.
| 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 `out_channels // in_channels`).
The parameters :attr:`kernel_size`, :attr:`stride`, :attr:`padding`, :attr:`dilation` can either be:
- a single ``int`` -- in which case the same value is used for the height and width dimension
- a ``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 configuration when `groups == in_channels` and `out_channels = K * in_channels`
where `K` is a positive integer is termed in literature as depthwise convolution.
In other words, for an input of size :math:`(N, C_{in}, H_{in}, W_{in})`, if you want a
depthwise convolution with a depthwise multiplier `K`,
then you use the constructor arguments
:math:`(in\_channels=C_{in}, out\_channels=C_{in} * K, ..., groups=C_{in})`
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
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: :math:`(N, C_{in}, H_{in}, W_{in})`
- Output: :math:`(N, C_{out}, H_{out}, W_{out})` where
:math:`H_{out} = floor((H_{in} + 2 * padding[0] - dilation[0] * (kernel\_size[0] - 1) - 1) / stride[0] + 1)`
:math:`W_{out} = floor((W_{in} + 2 * padding[1] - dilation[1] * (kernel\_size[1] - 1) - 1) / stride[1] + 1)`
Attributes:
weight (Tensor): the learnable weights of the module of shape
(out_channels, in_channels, kernel_size[0], kernel_size[1])
bias (Tensor): the learnable bias of the module of shape (out_channels)
Examples::
>>> # With square kernels and equal stride
>>> m = nn.Conv2d(16, 33, 3, stride=2)
>>> # non-square kernels and unequal stride and with padding
>>> m = nn.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
>>> # non-square kernels and unequal stride and with padding and dilation
>>> m = nn.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2), dilation=(3, 1))
>>> input = autograd.Variable(torch.randn(20, 16, 50, 100))
>>> output = m(input)
.. _cross-correlation:
https://en.wikipedia.org/wiki/Cross-correlation
.. _link:
https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md
"""
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True):
kernel_size = _pair(kernel_size)
stride = _pair(stride)
padding = _pair(padding)
dilation = _pair(dilation)
super(Conv2d, self).__init__(
in_channels, out_channels, kernel_size, stride, padding, dilation,
False, _pair(0), groups, bias)
def forward(self, input):
return F.conv2d(input, self.weight, self.bias, self.stride,
self.padding, self.dilation, self.groups)
[docs]class Conv3d(_ConvNd):
r"""Applies a 3D convolution over an input signal composed of several input
planes.
In the simplest case, the output value of the layer with input size :math:`(N, C_{in}, D, H, W)`
and output :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})` can be precisely described as:
.. math::
\begin{array}{ll}
out(N_i, C_{out_j}) = bias(C_{out_j})
+ \sum_{{k}=0}^{C_{in}-1} weight(C_{out_j}, k) \star input(N_i, k)
\end{array}
where :math:`\star` is the valid 3D `cross-correlation`_ operator
| :attr:`stride` controls the stride for the cross-correlation.
| :attr:`padding` controls the amount of implicit zero-paddings on both
| sides for :attr:`padding` number of points for each dimension.
| :attr:`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 what :attr:`dilation` does.
| :attr:`groups` controls the connections between inputs and outputs. `in_channels` and `out_channels`
must both be divisible by `groups`.
| 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 `out_channels // in_channels`).
The parameters :attr:`kernel_size`, :attr:`stride`, :attr:`padding`, :attr:`dilation` can either be:
- a single ``int`` -- in which case the same value is used for the depth, height and width dimension
- a ``tuple`` of three ints -- in which case, the first `int` is used for the depth dimension,
the second `int` for the height dimension and the third `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 configuration when `groups == in_channels` and `out_channels = K * in_channels`
where `K` is a positive integer is termed in literature as depthwise convolution.
In other words, for an input of size :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`, if you want a
depthwise convolution with a depthwise multiplier `K`,
then you use the constructor arguments
:math:`(in\_channels=C_{in}, out\_channels=C_{in} * K, ..., groups=C_{in})`
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 all three sides of the input. Default: 0
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: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`
- Output: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})` where
:math:`D_{out} = floor((D_{in} + 2 * padding[0] - dilation[0] * (kernel\_size[0] - 1) - 1) / stride[0] + 1)`
:math:`H_{out} = floor((H_{in} + 2 * padding[1] - dilation[1] * (kernel\_size[1] - 1) - 1) / stride[1] + 1)`
:math:`W_{out} = floor((W_{in} + 2 * padding[2] - dilation[2] * (kernel\_size[2] - 1) - 1) / stride[2] + 1)`
Attributes:
weight (Tensor): the learnable weights of the module of shape
(out_channels, in_channels, kernel_size[0], kernel_size[1], kernel_size[2])
bias (Tensor): the learnable bias of the module of shape (out_channels)
Examples::
>>> # With square kernels and equal stride
>>> m = nn.Conv3d(16, 33, 3, stride=2)
>>> # non-square kernels and unequal stride and with padding
>>> m = nn.Conv3d(16, 33, (3, 5, 2), stride=(2, 1, 1), padding=(4, 2, 0))
>>> input = autograd.Variable(torch.randn(20, 16, 10, 50, 100))
>>> output = m(input)
.. _cross-correlation:
https://en.wikipedia.org/wiki/Cross-correlation
.. _link:
https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md
"""
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True):
kernel_size = _triple(kernel_size)
stride = _triple(stride)
padding = _triple(padding)
dilation = _triple(dilation)
super(Conv3d, self).__init__(
in_channels, out_channels, kernel_size, stride, padding, dilation,
False, _triple(0), groups, bias)
def forward(self, input):
return F.conv3d(input, self.weight, self.bias, self.stride,
self.padding, self.dilation, self.groups)
class _ConvTransposeMixin(object):
def forward(self, input, output_size=None):
output_padding = self._output_padding(input, output_size)
func = self._backend.ConvNd(
self.stride, self.padding, self.dilation, self.transposed,
output_padding, self.groups)
if self.bias is None:
return func(input, self.weight)
else:
return func(input, self.weight, self.bias)
def _output_padding(self, input, output_size):
if output_size is None:
return self.output_padding
output_size = list(output_size)
k = input.dim() - 2
if len(output_size) == k + 2:
output_size = output_size[-2:]
if len(output_size) != k:
raise ValueError(
"output_size must have {} or {} elements (got {})"
.format(k, k + 2, len(output_size)))
def dim_size(d):
return ((input.size(d + 2) - 1) * self.stride[d] -
2 * self.padding[d] + self.kernel_size[d])
min_sizes = [dim_size(d) for d in range(k)]
max_sizes = [min_sizes[d] + self.stride[d] - 1 for d in range(k)]
for size, min_size, max_size in zip(output_size, min_sizes, max_sizes):
if size < min_size or size > max_size:
raise ValueError((
"requested an output size of {}, but valid sizes range "
"from {} to {} (for an input of {})").format(
output_size, min_sizes, max_sizes, input.size()[2:]))
return tuple([output_size[d] - min_sizes[d] for d in range(k)])
[docs]class ConvTranspose1d(_ConvTransposeMixin, _ConvNd):
r"""Applies a 1D transposed convolution operator over an input image
composed of several input planes.
This module can be seen as the gradient of Conv1d 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).
| :attr:`stride` controls the stride for the cross-correlation.
| :attr:`padding` controls the amount of implicit zero-paddings on both
| sides for :attr:`padding` number of points.
| :attr:`output_padding` controls the amount of implicit zero-paddings on
| both sides of the output for :attr:`output_padding` number of points.
| number of points.
| :attr:`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 what :attr:`dilation` does.
| :attr:`groups` controls the connections between inputs and outputs. `in_channels` and `out_channels`
must both be divisible by `groups`.
| 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 `out_channels // in_channels`).
.. 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.
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
output_padding (int or tuple, optional): Zero-padding added to one side of the output. 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: :math:`(N, C_{in}, L_{in})`
- Output: :math:`(N, C_{out}, L_{out})` where
:math:`L_{out} = (L_{in} - 1) * stride - 2 * padding + kernel\_size + output\_padding`
Attributes:
weight (Tensor): the learnable weights of the module of shape
(in_channels, out_channels, kernel_size[0], kernel_size[1])
bias (Tensor): the learnable bias of the module of shape (out_channels)
"""
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, output_padding=0, groups=1, bias=True, dilation=1):
kernel_size = _single(kernel_size)
stride = _single(stride)
padding = _single(padding)
dilation = _single(dilation)
output_padding = _single(output_padding)
super(ConvTranspose1d, self).__init__(
in_channels, out_channels, kernel_size, stride, padding, dilation,
True, output_padding, groups, bias)
def forward(self, input, output_size=None):
output_padding = self._output_padding(input, output_size)
return F.conv_transpose1d(
input, self.weight, self.bias, self.stride, self.padding,
output_padding, self.groups, self.dilation)
[docs]class ConvTranspose2d(_ConvTransposeMixin, _ConvNd):
r"""Applies a 2D transposed convolution operator over an input image
composed of several input planes.
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).
| :attr:`stride` controls the stride for the cross-correlation.
| :attr:`padding` controls the amount of implicit zero-paddings on both
| sides for :attr:`padding` number of points for each dimension.
| :attr:`output_padding` controls the amount of implicit zero-paddings on
| both sides of the output for :attr:`output_padding` number of points for
| each dimension.
| :attr:`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 what :attr:`dilation` does.
| :attr:`groups` controls the connections between inputs and outputs. `in_channels` and `out_channels`
must both be divisible by `groups`.
| 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 `out_channels // in_channels`).
The parameters :attr:`kernel_size`, :attr:`stride`, :attr:`padding`, :attr:`output_padding`
can either be:
- a single ``int`` -- in which case the same value is used for the height and width dimensions
- a ``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.
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
output_padding (int or tuple, optional): Zero-padding added to one side of the output. 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: :math:`(N, C_{in}, H_{in}, W_{in})`
- Output: :math:`(N, C_{out}, H_{out}, W_{out})` where
:math:`H_{out} = (H_{in} - 1) * stride[0] - 2 * padding[0] + kernel\_size[0] + output\_padding[0]`
:math:`W_{out} = (W_{in} - 1) * stride[1] - 2 * padding[1] + kernel\_size[1] + output\_padding[1]`
Attributes:
weight (Tensor): the learnable weights of the module of shape
(in_channels, out_channels, kernel_size[0], kernel_size[1])
bias (Tensor): the learnable bias of the module of shape (out_channels)
Examples::
>>> # With square kernels and equal stride
>>> m = nn.ConvTranspose2d(16, 33, 3, stride=2)
>>> # non-square kernels and unequal stride and with padding
>>> m = nn.ConvTranspose2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
>>> input = autograd.Variable(torch.randn(20, 16, 50, 100))
>>> output = m(input)
>>> # exact output size can be also specified as an argument
>>> input = autograd.Variable(torch.randn(1, 16, 12, 12))
>>> downsample = nn.Conv2d(16, 16, 3, stride=2, padding=1)
>>> upsample = nn.ConvTranspose2d(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])
.. _cross-correlation:
https://en.wikipedia.org/wiki/Cross-correlation
.. _link:
https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md
"""
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, output_padding=0, groups=1, bias=True, dilation=1):
kernel_size = _pair(kernel_size)
stride = _pair(stride)
padding = _pair(padding)
dilation = _pair(dilation)
output_padding = _pair(output_padding)
super(ConvTranspose2d, self).__init__(
in_channels, out_channels, kernel_size, stride, padding, dilation,
True, output_padding, groups, bias)
def forward(self, input, output_size=None):
output_padding = self._output_padding(input, output_size)
return F.conv_transpose2d(
input, self.weight, self.bias, self.stride, self.padding,
output_padding, self.groups, self.dilation)
[docs]class ConvTranspose3d(_ConvTransposeMixin, _ConvNd):
r"""Applies a 3D transposed convolution operator over an input image composed of several input
planes.
The transposed convolution operator multiplies each input value element-wise by a learnable kernel,
and sums over the outputs from all input feature planes.
This module can be seen as the gradient of Conv3d 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).
| :attr:`stride` controls the stride for the cross-correlation.
| :attr:`padding` controls the amount of implicit zero-paddings on both
| sides for :attr:`padding` number of points for each dimension.
| :attr:`output_padding` controls the amount of implicit zero-paddings on
| both sides of the output for :attr:`output_padding` number of points for
| each dimension.
| :attr:`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 what :attr:`dilation` does.
| :attr:`groups` controls the connections between inputs and outputs. `in_channels` and `out_channels`
must both be divisible by `groups`.
| 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 `out_channels // in_channels`).
The parameters :attr:`kernel_size`, :attr:`stride`, :attr:`padding`, :attr:`output_padding`
can either be:
- a single ``int`` -- in which case the same value is used for the depth, height and width dimensions
- a ``tuple`` of three ints -- in which case, the first `int` is used for the depth dimension,
the second `int` for the height dimension and the third `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.
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 all three sides of the input. Default: 0
output_padding (int or tuple, optional): Zero-padding added to one side of the output. 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: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`
- Output: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})` where
:math:`D_{out} = (D_{in} - 1) * stride[0] - 2 * padding[0] + kernel\_size[0] + output\_padding[0]`
:math:`H_{out} = (H_{in} - 1) * stride[1] - 2 * padding[1] + kernel\_size[1] + output\_padding[1]`
:math:`W_{out} = (W_{in} - 1) * stride[2] - 2 * padding[2] + kernel\_size[2] + output\_padding[2]`
Attributes:
weight (Tensor): the learnable weights of the module of shape
(in_channels, out_channels, kernel_size[0], kernel_size[1], kernel_size[2])
bias (Tensor): the learnable bias of the module of shape (out_channels)
Examples::
>>> # With square kernels and equal stride
>>> m = nn.ConvTranspose3d(16, 33, 3, stride=2)
>>> # non-square kernels and unequal stride and with padding
>>> m = nn.Conv3d(16, 33, (3, 5, 2), stride=(2, 1, 1), padding=(0, 4, 2))
>>> input = autograd.Variable(torch.randn(20, 16, 10, 50, 100))
>>> output = m(input)
.. _cross-correlation:
https://en.wikipedia.org/wiki/Cross-correlation
.. _link:
https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md
"""
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, output_padding=0, groups=1, bias=True, dilation=1):
kernel_size = _triple(kernel_size)
stride = _triple(stride)
padding = _triple(padding)
dilation = _triple(dilation)
output_padding = _triple(output_padding)
super(ConvTranspose3d, self).__init__(
in_channels, out_channels, kernel_size, stride, padding, dilation,
True, output_padding, groups, bias)
def forward(self, input, output_size=None):
output_padding = self._output_padding(input, output_size)
return F.conv_transpose3d(
input, self.weight, self.bias, self.stride, self.padding,
output_padding, self.groups, self.dilation)
# TODO: Conv2dLocal
# TODO: Conv2dMap
# TODO: ConvTranspose2dMap
```