# Source code for torch.nn.modules.conv

```
# coding=utf-8
import math
import warnings
import torch
from torch import Tensor
from torch.nn.parameter import Parameter
from .. import functional as F
from .. import init
from .module import Module
from .utils import _single, _pair, _triple, _reverse_repeat_tuple
from ..common_types import _size_1_t, _size_2_t, _size_3_t
from typing import Optional, List, Tuple
class _ConvNd(Module):
__constants__ = ['stride', 'padding', 'dilation', 'groups',
'padding_mode', 'output_padding', 'in_channels',
'out_channels', 'kernel_size']
__annotations__ = {'bias': Optional[torch.Tensor]}
_in_channels: int
out_channels: int
kernel_size: Tuple[int, ...]
stride: Tuple[int, ...]
padding: Tuple[int, ...]
dilation: Tuple[int, ...]
transposed: bool
output_padding: Tuple[int, ...]
groups: int
padding_mode: str
weight: Tensor
bias: Optional[Tensor]
def __init__(self,
in_channels: int,
out_channels: int,
kernel_size: _size_1_t,
stride: _size_1_t,
padding: _size_1_t,
dilation: _size_1_t,
transposed: bool,
output_padding: _size_1_t,
groups: int,
bias: Optional[Tensor],
padding_mode: str) -> None:
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')
valid_padding_modes = {'zeros', 'reflect', 'replicate', 'circular'}
if padding_mode not in valid_padding_modes:
raise ValueError("padding_mode must be one of {}, but got padding_mode='{}'".format(
valid_padding_modes, padding_mode))
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
self.padding_mode = padding_mode
# `_reversed_padding_repeated_twice` is the padding to be passed to
# `F.pad` if needed (e.g., for non-zero padding types that are
# implemented as two ops: padding + conv). `F.pad` accepts paddings in
# reverse order than the dimension.
self._reversed_padding_repeated_twice = _reverse_repeat_tuple(self.padding, 2)
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) -> None:
init.kaiming_uniform_(self.weight, a=math.sqrt(5))
if self.bias is not None:
fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
bound = 1 / math.sqrt(fan_in)
init.uniform_(self.bias, -bound, bound)
def extra_repr(self):
s = ('{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'
if self.padding_mode != 'zeros':
s += ', padding_mode={padding_mode}'
return s.format(**self.__dict__)
def __setstate__(self, state):
super(_ConvNd, self).__setstate__(state)
if not hasattr(self, 'padding_mode'):
self.padding_mode = 'zeros'
[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_{\text{in}}, L)` and output :math:`(N, C_{\text{out}}, L_{\text{out}})` 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 :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.
:attr:`in_channels` and :attr:`out_channels` must both be divisible by
:attr:`groups`. 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= :attr:`in_channels`, each input channel is convolved with
its own set of filters,
of size
:math:`\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor`.
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 :math:`(N, C_{in}, L_{in})`,
a depthwise convolution with a depthwise multiplier `K`, can be constructed by arguments
:math:`(C_\text{in}=C_{in}, C_\text{out}=C_{in} \times K, ..., \text{groups}=C_{in})`.
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 :doc:`/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``
Shape:
- Input: :math:`(N, C_{in}, L_{in})`
- Output: :math:`(N, C_{out}, L_{out})` 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
Attributes:
weight (Tensor): the learnable weights of the module of shape
:math:`(\text{out\_channels},
\frac{\text{in\_channels}}{\text{groups}}, \text{kernel\_size})`.
The values of these weights are sampled from
:math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
:math:`k = \frac{groups}{C_\text{in} * \text{kernel\_size}}`
bias (Tensor): the learnable bias of the module of shape
(out_channels). If :attr:`bias` is ``True``, then the values of these weights are
sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
:math:`k = \frac{groups}{C_\text{in} * \text{kernel\_size}}`
Examples::
>>> m = nn.Conv1d(16, 33, 3, stride=2)
>>> input = 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: int,
out_channels: int,
kernel_size: _size_1_t,
stride: _size_1_t = 1,
padding: _size_1_t = 0,
dilation: _size_1_t = 1,
groups: int = 1,
bias: bool = True,
padding_mode: str = 'zeros' # TODO: refine this type
):
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, padding_mode)
def forward(self, input: Tensor) -> Tensor:
if self.padding_mode != 'zeros':
return F.conv1d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
self.weight, self.bias, self.stride,
_single(0), self.dilation, self.groups)
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_{\text{in}}, H, W)` and output :math:`(N, C_{\text{out}}, H_{\text{out}}, W_{\text{out}})`
can be precisely described as:
.. math::
\text{out}(N_i, C_{\text{out}_j}) = \text{bias}(C_{\text{out}_j}) +
\sum_{k = 0}^{C_{\text{in}} - 1} \text{weight}(C_{\text{out}_j}, k) \star \text{input}(N_i, k)
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.
:attr:`in_channels` and :attr:`out_channels` must both be divisible by
:attr:`groups`. 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= :attr:`in_channels`, each input channel is convolved with
its own set of filters, of size:
:math:`\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor`.
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:
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 :math:`(N, C_{in}, H_{in}, W_{in})`,
a depthwise convolution with a depthwise multiplier `K`, can be constructed by arguments
:math:`(in\_channels=C_{in}, out\_channels=C_{in} \times K, ..., groups=C_{in})`.
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 :doc:`/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``
Shape:
- Input: :math:`(N, C_{in}, H_{in}, W_{in})`
- Output: :math:`(N, C_{out}, H_{out}, W_{out})` where
.. math::
H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[0] - \text{dilation}[0]
\times (\text{kernel\_size}[0] - 1) - 1}{\text{stride}[0]} + 1\right\rfloor
.. math::
W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding}[1] - \text{dilation}[1]
\times (\text{kernel\_size}[1] - 1) - 1}{\text{stride}[1]} + 1\right\rfloor
Attributes:
weight (Tensor): the learnable weights of the module of shape
:math:`(\text{out\_channels}, \frac{\text{in\_channels}}{\text{groups}},`
:math:`\text{kernel\_size[0]}, \text{kernel\_size[1]})`.
The values of these weights are sampled from
:math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
:math:`k = \frac{groups}{C_\text{in} * \prod_{i=0}^{1}\text{kernel\_size}[i]}`
bias (Tensor): the learnable bias of the module of shape
(out_channels). If :attr:`bias` is ``True``,
then the values of these weights are
sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
:math:`k = \frac{groups}{C_\text{in} * \prod_{i=0}^{1}\text{kernel\_size}[i]}`
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 = 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: int,
out_channels: int,
kernel_size: _size_2_t,
stride: _size_2_t = 1,
padding: _size_2_t = 0,
dilation: _size_2_t = 1,
groups: int = 1,
bias: bool = True,
padding_mode: str = 'zeros' # TODO: refine this type
):
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, padding_mode)
def _conv_forward(self, input, weight):
if self.padding_mode != 'zeros':
return F.conv2d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
weight, self.bias, self.stride,
_pair(0), self.dilation, self.groups)
return F.conv2d(input, weight, self.bias, self.stride,
self.padding, self.dilation, self.groups)
def forward(self, input: Tensor) -> Tensor:
return self._conv_forward(input, self.weight)
[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::
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)
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.
:attr:`in_channels` and :attr:`out_channels` must both be divisible by
:attr:`groups`. 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= :attr:`in_channels`, each input channel is convolved with
its own set of filters, of size
:math:`\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor`.
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:
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 :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`,
a depthwise convolution with a depthwise multiplier `K`, can be constructed by arguments
:math:`(in\_channels=C_{in}, out\_channels=C_{in} \times K, ..., groups=C_{in})`.
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 :doc:`/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 all three 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: :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} = \left\lfloor\frac{D_{in} + 2 \times \text{padding}[0] - \text{dilation}[0]
\times (\text{kernel\_size}[0] - 1) - 1}{\text{stride}[0]} + 1\right\rfloor
.. math::
H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[1] - \text{dilation}[1]
\times (\text{kernel\_size}[1] - 1) - 1}{\text{stride}[1]} + 1\right\rfloor
.. math::
W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding}[2] - \text{dilation}[2]
\times (\text{kernel\_size}[2] - 1) - 1}{\text{stride}[2]} + 1\right\rfloor
Attributes:
weight (Tensor): the learnable weights of the module of shape
:math:`(\text{out\_channels}, \frac{\text{in\_channels}}{\text{groups}},`
:math:`\text{kernel\_size[0]}, \text{kernel\_size[1]}, \text{kernel\_size[2]})`.
The values of these weights are sampled from
:math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
:math:`k = \frac{groups}{C_\text{in} * \prod_{i=0}^{2}\text{kernel\_size}[i]}`
bias (Tensor): the learnable bias of the module of shape (out_channels). If :attr:`bias` is ``True``,
then the values of these weights are
sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
:math:`k = \frac{groups}{C_\text{in} * \prod_{i=0}^{2}\text{kernel\_size}[i]}`
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 = 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: int,
out_channels: int,
kernel_size: _size_3_t,
stride: _size_3_t = 1,
padding: _size_3_t = 0,
dilation: _size_3_t = 1,
groups: int = 1,
bias: bool = True,
padding_mode: str = 'zeros'
):
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, padding_mode)
def forward(self, input: Tensor) -> Tensor:
if self.padding_mode != 'zeros':
return F.conv3d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
self.weight, self.bias, self.stride, _triple(0),
self.dilation, self.groups)
return F.conv3d(input, self.weight, self.bias, self.stride,
self.padding, self.dilation, self.groups)
class _ConvTransposeNd(_ConvNd):
def __init__(self, in_channels, out_channels, kernel_size, stride,
padding, dilation, transposed, output_padding,
groups, bias, padding_mode):
if padding_mode != 'zeros':
raise ValueError('Only "zeros" padding mode is supported for {}'.format(self.__class__.__name__))
super(_ConvTransposeNd, self).__init__(
in_channels, out_channels, kernel_size, stride,
padding, dilation, transposed, output_padding,
groups, bias, padding_mode)
def _output_padding(self, input, output_size, stride, padding, kernel_size):
# type: (Tensor, Optional[List[int]], List[int], List[int], List[int]) -> List[int]
if output_size is None:
ret = _single(self.output_padding) # converting to list if was not already
else:
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)))
min_sizes = torch.jit.annotate(List[int], [])
max_sizes = torch.jit.annotate(List[int], [])
for d in range(k):
dim_size = ((input.size(d + 2) - 1) * stride[d] -
2 * padding[d] + kernel_size[d])
min_sizes.append(dim_size)
max_sizes.append(min_sizes[d] + stride[d] - 1)
for i in range(len(output_size)):
size = output_size[i]
min_size = min_sizes[i]
max_size = max_sizes[i]
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:]))
res = torch.jit.annotate(List[int], [])
for d in range(k):
res.append(output_size[d] - min_sizes[d])
ret = res
return ret
[docs]class ConvTranspose1d(_ConvTransposeNd):
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 ``dilation * (kernel_size - 1) - padding`` number of points. See note
below for details.
* :attr:`output_padding` controls the additional size added to one side
of the output shape. See note below for details.
* :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.
:attr:`in_channels` and :attr:`out_channels` must both be divisible by
:attr:`groups`. 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= :attr:`in_channels`, each input channel is convolved with
its own set of filters (of size
:math:`\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor`).
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 :attr:`padding` argument effectively adds ``dilation * (kernel_size - 1) - padding``
amount of zero padding to both sizes of the input. This is set so that
when a :class:`~torch.nn.Conv1d` and a :class:`~torch.nn.ConvTranspose1d`
are initialized with same parameters, they are inverses of each other in
regard to the input and output shapes. However, when ``stride > 1``,
:class:`~torch.nn.Conv1d` maps multiple input shapes to the same output
shape. :attr:`output_padding` is provided to resolve this ambiguity by
effectively increasing the calculated output shape on one side. Note
that :attr:`output_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 :doc:`/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-padding
will be added to both sides of the input. Default: 0
output_padding (int or tuple, optional): Additional size added to one side
of 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: :math:`(N, C_{in}, L_{in})`
- Output: :math:`(N, C_{out}, L_{out})` where
.. math::
L_{out} = (L_{in} - 1) \times \text{stride} - 2 \times \text{padding} + \text{dilation}
\times (\text{kernel\_size} - 1) + \text{output\_padding} + 1
Attributes:
weight (Tensor): the learnable weights of the module of shape
:math:`(\text{in\_channels}, \frac{\text{out\_channels}}{\text{groups}},`
:math:`\text{kernel\_size})`.
The values of these weights are sampled from
:math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
:math:`k = \frac{groups}{C_\text{out} * \text{kernel\_size}}`
bias (Tensor): the learnable bias of the module of shape (out_channels).
If :attr:`bias` is ``True``, then the values of these weights are
sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
:math:`k = \frac{groups}{C_\text{out} * \text{kernel\_size}}`
"""
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: _size_1_t,
stride: _size_1_t = 1,
padding: _size_1_t = 0,
output_padding: _size_1_t = 0,
groups: int = 1,
bias: bool = True,
dilation: _size_1_t = 1,
padding_mode: str = 'zeros'
):
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, padding_mode)
def forward(self, input: Tensor, output_size: Optional[List[int]] = None) -> Tensor:
if self.padding_mode != 'zeros':
raise ValueError('Only `zeros` padding mode is supported for ConvTranspose1d')
output_padding = self._output_padding(input, output_size, self.stride, self.padding, self.kernel_size)
return F.conv_transpose1d(
input, self.weight, self.bias, self.stride, self.padding,
output_padding, self.groups, self.dilation)
[docs]class ConvTranspose2d(_ConvTransposeNd):
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 ``dilation * (kernel_size - 1) - padding`` number of points. See note
below for details.
* :attr:`output_padding` controls the additional size added to one side
of the output shape. See note below for details.
* :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.
:attr:`in_channels` and :attr:`out_channels` must both be divisible by
:attr:`groups`. 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= :attr:`in_channels`, each input channel is convolved with
its own set of filters (of size
:math:`\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor`).
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.
Note:
The :attr:`padding` argument effectively adds ``dilation * (kernel_size - 1) - padding``
amount of zero padding to both sizes of the input. This is set so that
when a :class:`~torch.nn.Conv2d` and a :class:`~torch.nn.ConvTranspose2d`
are initialized with same parameters, they are inverses of each other in
regard to the input and output shapes. However, when ``stride > 1``,
:class:`~torch.nn.Conv2d` maps multiple input shapes to the same output
shape. :attr:`output_padding` is provided to resolve this ambiguity by
effectively increasing the calculated output shape on one side. Note
that :attr:`output_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 :doc:`/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-padding
will 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: :math:`(N, C_{in}, H_{in}, W_{in})`
- Output: :math:`(N, C_{out}, H_{out}, W_{out})` where
.. math::
H_{out} = (H_{in} - 1) \times \text{stride}[0] - 2 \times \text{padding}[0] + \text{dilation}[0]
\times (\text{kernel\_size}[0] - 1) + \text{output\_padding}[0] + 1
.. math::
W_{out} = (W_{in} - 1) \times \text{stride}[1] - 2 \times \text{padding}[1] + \text{dilation}[1]
\times (\text{kernel\_size}[1] - 1) + \text{output\_padding}[1] + 1
Attributes:
weight (Tensor): the learnable weights of the module of shape
:math:`(\text{in\_channels}, \frac{\text{out\_channels}}{\text{groups}},`
:math:`\text{kernel\_size[0]}, \text{kernel\_size[1]})`.
The values of these weights are sampled from
:math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
:math:`k = \frac{groups}{C_\text{out} * \prod_{i=0}^{1}\text{kernel\_size}[i]}`
bias (Tensor): the learnable bias of the module of shape (out_channels)
If :attr:`bias` is ``True``, then the values of these weights are
sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
:math:`k = \frac{groups}{C_\text{out} * \prod_{i=0}^{1}\text{kernel\_size}[i]}`
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 = 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 = 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: int,
out_channels: int,
kernel_size: _size_2_t,
stride: _size_2_t = 1,
padding: _size_2_t = 0,
output_padding: _size_2_t = 0,
groups: int = 1,
bias: bool = True,
dilation: int = 1,
padding_mode: str = 'zeros'
):
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, padding_mode)
def forward(self, input: Tensor, output_size: Optional[List[int]] = None) -> Tensor:
if self.padding_mode != 'zeros':
raise ValueError('Only `zeros` padding mode is supported for ConvTranspose2d')
output_padding = self._output_padding(input, output_size, self.stride, self.padding, self.kernel_size)
return F.conv_transpose2d(
input, self.weight, self.bias, self.stride, self.padding,
output_padding, self.groups, self.dilation)
[docs]class ConvTranspose3d(_ConvTransposeNd):
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 ``dilation * (kernel_size - 1) - padding`` number of points. See note
below for details.
* :attr:`output_padding` controls the additional size added to one side
of the output shape. See note below for details.
* :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.
:attr:`in_channels` and :attr:`out_channels` must both be divisible by
:attr:`groups`. 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= :attr:`in_channels`, each input channel is convolved with
its own set of filters (of size
:math:`\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor`).
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.
Note:
The :attr:`padding` argument effectively adds ``dilation * (kernel_size - 1) - padding``
amount of zero padding to both sizes of the input. This is set so that
when a :class:`~torch.nn.Conv3d` and a :class:`~torch.nn.ConvTranspose3d`
are initialized with same parameters, they are inverses of each other in
regard to the input and output shapes. However, when ``stride > 1``,
:class:`~torch.nn.Conv3d` maps multiple input shapes to the same output
shape. :attr:`output_padding` is provided to resolve this ambiguity by
effectively increasing the calculated output shape on one side. Note
that :attr:`output_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 :doc:`/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-padding
will 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: :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) \times \text{stride}[0] - 2 \times \text{padding}[0] + \text{dilation}[0]
\times (\text{kernel\_size}[0] - 1) + \text{output\_padding}[0] + 1
.. math::
H_{out} = (H_{in} - 1) \times \text{stride}[1] - 2 \times \text{padding}[1] + \text{dilation}[1]
\times (\text{kernel\_size}[1] - 1) + \text{output\_padding}[1] + 1
.. math::
W_{out} = (W_{in} - 1) \times \text{stride}[2] - 2 \times \text{padding}[2] + \text{dilation}[2]
\times (\text{kernel\_size}[2] - 1) + \text{output\_padding}[2] + 1
Attributes:
weight (Tensor): the learnable weights of the module of shape
:math:`(\text{in\_channels}, \frac{\text{out\_channels}}{\text{groups}},`
:math:`\text{kernel\_size[0]}, \text{kernel\_size[1]}, \text{kernel\_size[2]})`.
The values of these weights are sampled from
:math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
:math:`k = \frac{groups}{C_\text{out} * \prod_{i=0}^{2}\text{kernel\_size}[i]}`
bias (Tensor): the learnable bias of the module of shape (out_channels)
If :attr:`bias` is ``True``, then the values of these weights are
sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
:math:`k = \frac{groups}{C_\text{out} * \prod_{i=0}^{2}\text{kernel\_size}[i]}`
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.ConvTranspose3d(16, 33, (3, 5, 2), stride=(2, 1, 1), padding=(0, 4, 2))
>>> input = 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: int,
out_channels: int,
kernel_size: _size_3_t,
stride: _size_3_t = 1,
padding: _size_3_t = 0,
output_padding: _size_3_t = 0,
groups: int = 1,
bias: bool = True,
dilation: _size_3_t = 1,
padding_mode: str = 'zeros'
):
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, padding_mode)
def forward(self, input: Tensor, output_size: Optional[List[int]] = None) -> Tensor:
if self.padding_mode != 'zeros':
raise ValueError('Only `zeros` padding mode is supported for ConvTranspose3d')
output_padding = self._output_padding(input, output_size, self.stride, self.padding, self.kernel_size)
return F.conv_transpose3d(
input, self.weight, self.bias, self.stride, self.padding,
output_padding, self.groups, self.dilation)
# TODO: Deprecate and remove the following alias `_ConvTransposeMixin`.
#
# `_ConvTransposeMixin` was a mixin that was removed. It is meant to be used
# with `_ConvNd` to construct actual module classes that implements conv
# transpose ops:
#
# class MyConvTranspose(_ConvNd, _ConvTransposeMixin):
# ...
#
# In PyTorch, it has been replaced by `_ConvTransposeNd`, which is a proper
# subclass of `_ConvNd`. However, some user code in the wild still (incorrectly)
# use the internal class `_ConvTransposeMixin`. Hence, we provide this alias
# for BC, because it is cheap and easy for us to do so, even though that
# `_ConvTransposeNd` is really not a mixin anymore (but multiple inheritance as
# above would still work).
class _ConvTransposeMixin(_ConvTransposeNd):
def __init__(self, *args, **kwargs):
warnings.warn(
"_ConvTransposeMixin is a deprecated internal class. "
"Please consider using public APIs.")
super(_ConvTransposeMixin, self).__init__(*args, **kwargs)
# TODO: Conv2dLocal
# TODO: Conv2dMap
# TODO: ConvTranspose2dMap
```