torch.nn.modules.conv — PyTorch 2.7 documentation (original) (raw)

mypy: allow-untyped-defs

import math from typing import Optional, Union from typing_extensions import deprecated

import torch from torch import Tensor from torch._torch_docs import reproducibility_notes from torch.nn import functional as F, init from torch.nn.common_types import _size_1_t, _size_2_t, _size_3_t from torch.nn.parameter import Parameter, UninitializedParameter

from .lazy import LazyModuleMixin from .module import Module from .utils import _pair, _reverse_repeat_tuple, _single, _triple

all = [ "Conv1d", "Conv2d", "Conv3d", "ConvTranspose1d", "ConvTranspose2d", "ConvTranspose3d", "LazyConv1d", "LazyConv2d", "LazyConv3d", "LazyConvTranspose1d", "LazyConvTranspose2d", "LazyConvTranspose3d", ]

convolution_notes = { "groups_note": r"""* :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:`\frac{\text{out\_channels}}{\text{in\_channels}}`).""",
"depthwise_separable_note": r"""When `groups == in_channels` and `out_channels == K * in_channels`,
    where `K` is a positive integer, this operation is also known as a "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 performed with the arguments
    :math:`(C_\text{in}=C_\text{in}, C_\text{out}=C_\text{in} \times \text{K}, ..., \text{groups}=C_\text{in})`.""",

} # noqa: B950

class _ConvNd(Module): constants = [ "stride", "padding", "dilation", "groups", "padding_mode", "output_padding", "in_channels", "out_channels", "kernel_size", ] annotations = {"bias": Optional[torch.Tensor]}

def _conv_forward(self, input: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor:  # type: ignore[empty-body]
    ...

in_channels: int
_reversed_padding_repeated_twice: list[int]
out_channels: int
kernel_size: tuple[int, ...]
stride: tuple[int, ...]
padding: Union[str, 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: tuple[int, ...],
    stride: tuple[int, ...],
    padding: Union[str, tuple[int, ...]],
    dilation: tuple[int, ...],
    transposed: bool,
    output_padding: tuple[int, ...],
    groups: int,
    bias: bool,
    padding_mode: str,
    device=None,
    dtype=None,
) -> None:
    factory_kwargs = {"device": device, "dtype": dtype}
    super().__init__()
    if groups <= 0:
        raise ValueError("groups must be a positive integer")
    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_strings = {"same", "valid"}
    if isinstance(padding, str):
        if padding not in valid_padding_strings:
            raise ValueError(
                f"Invalid padding string {padding!r}, should be one of {valid_padding_strings}"
            )
        if padding == "same" and any(s != 1 for s in stride):
            raise ValueError(
                "padding='same' is not supported for strided convolutions"
            )

    valid_padding_modes = {"zeros", "reflect", "replicate", "circular"}
    if padding_mode not in valid_padding_modes:
        raise ValueError(
            f"padding_mode must be one of {valid_padding_modes}, but got padding_mode='{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.
    if isinstance(self.padding, str):
        self._reversed_padding_repeated_twice = [0, 0] * len(kernel_size)
        if padding == "same":
            for d, k, i in zip(
                dilation, kernel_size, range(len(kernel_size) - 1, -1, -1)
            ):
                total_padding = d * (k - 1)
                left_pad = total_padding // 2
                self._reversed_padding_repeated_twice[2 * i] = left_pad
                self._reversed_padding_repeated_twice[2 * i + 1] = (
                    total_padding - left_pad
                )
    else:
        self._reversed_padding_repeated_twice = _reverse_repeat_tuple(
            self.padding, 2
        )

    if transposed:
        self.weight = Parameter(
            torch.empty(
                (in_channels, out_channels // groups, *kernel_size),
                **factory_kwargs,
            )
        )
    else:
        self.weight = Parameter(
            torch.empty(
                (out_channels, in_channels // groups, *kernel_size),
                **factory_kwargs,
            )
        )
    if bias:
        self.bias = Parameter(torch.empty(out_channels, **factory_kwargs))
    else:
        self.register_parameter("bias", None)

    self.reset_parameters()

def reset_parameters(self) -> None:
    # Setting a=sqrt(5) in kaiming_uniform is the same as initializing with
    # uniform(-1/sqrt(k), 1/sqrt(k)), where k = weight.size(1) * prod(*kernel_size)
    # For more details see: https://github.com/pytorch/pytorch/issues/15314#issuecomment-477448573
    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)
        if fan_in != 0:
            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().__setstate__(state)
    if not hasattr(self, "padding_mode"):
        self.padding_mode = "zeros"

[docs]class Conv1d(_ConvNd): doc = ( 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.
"""
    + r"""

This module supports :ref:`TensorFloat32<tf32_on_ampere>`.

On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision<fp16_on_mi200>` for backward.

* :attr:`stride` controls the stride for the cross-correlation, a single
  number or a one-element tuple.

* :attr:`padding` controls the amount of padding applied to the input. It
  can be either a string {{'valid', 'same'}} or a tuple of ints giving the
  amount of implicit padding applied on both sides.

""" """ * :attr:dilation controls the spacing between the kernel points; also known as the \u00e0 trous algorithm. It is harder to describe, but this link_ has a nice visualization of what :attr:dilation does. """ r""" {groups_note}

Note:
    {depthwise_separable_note}
Note:
    {cudnn_reproducibility_note}

Note:
    ``padding='valid'`` is the same as no padding. ``padding='same'`` pads
    the input so the output has the shape as the input. However, this mode
    doesn't support any stride values other than 1.

Note:
    This module supports complex data types i.e. ``complex32, complex64, complex128``.

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, tuple or str, optional): 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``
    padding_mode (str, optional): ``'zeros'``, ``'reflect'``,
        ``'replicate'`` or ``'circular'``. Default: ``'zeros'``

""".format(
        **reproducibility_notes, **convolution_notes
    )
    + r"""

Shape:
    - Input: :math:`(N, C_{in}, L_{in})` or :math:`(C_{in}, L_{in})`
    - Output: :math:`(N, C_{out}, L_{out})` or :math:`(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: Union[str, _size_1_t] = 0,
    dilation: _size_1_t = 1,
    groups: int = 1,
    bias: bool = True,
    padding_mode: str = "zeros",  # TODO: refine this type
    device=None,
    dtype=None,
) -> None:
    factory_kwargs = {"device": device, "dtype": dtype}
    # we create new variables below to make mypy happy since kernel_size has
    # type Union[int, Tuple[int]] and kernel_size_ has type Tuple[int]
    kernel_size_ = _single(kernel_size)
    stride_ = _single(stride)
    padding_ = padding if isinstance(padding, str) else _single(padding)
    dilation_ = _single(dilation)
    super().__init__(
        in_channels,
        out_channels,
        kernel_size_,
        stride_,
        padding_,
        dilation_,
        False,
        _single(0),
        groups,
        bias,
        padding_mode,
        **factory_kwargs,
    )

def _conv_forward(self, input: Tensor, weight: Tensor, bias: Optional[Tensor]):
    if self.padding_mode != "zeros":
        return F.conv1d(
            F.pad(
                input, self._reversed_padding_repeated_twice, mode=self.padding_mode
            ),
            weight,
            bias,
            self.stride,
            _single(0),
            self.dilation,
            self.groups,
        )
    return F.conv1d(
        input, weight, bias, self.stride, self.padding, self.dilation, self.groups
    )

def forward(self, input: Tensor) -> Tensor:
    return self._conv_forward(input, self.weight, self.bias)

[docs]class Conv2d(_ConvNd): doc = ( 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.
"""
    + r"""

This module supports :ref:`TensorFloat32<tf32_on_ampere>`.

On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision<fp16_on_mi200>` for backward.

* :attr:`stride` controls the stride for the cross-correlation, a single
  number or a tuple.

* :attr:`padding` controls the amount of padding applied to the input. It
  can be either a string {{'valid', 'same'}} or an int / a tuple of ints giving the
  amount of implicit padding applied on both sides.

""" """ * :attr:dilation controls the spacing between the kernel points; also known as the \u00e0 trous algorithm. It is harder to describe, but this link_ has a nice visualization of what :attr:dilation does. """ r"""

{groups_note}

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:
    {depthwise_separable_note}

Note:
    {cudnn_reproducibility_note}

Note:
    ``padding='valid'`` is the same as no padding. ``padding='same'`` pads
    the input so the output has the shape as the input. However, this mode
    doesn't support any stride values other than 1.

Note:
    This module supports complex data types i.e. ``complex32, complex64, complex128``.

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, tuple or str, optional): Padding added to all four 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``
    padding_mode (str, optional): ``'zeros'``, ``'reflect'``,
        ``'replicate'`` or ``'circular'``. Default: ``'zeros'``
""".format(
        **reproducibility_notes, **convolution_notes
    )
    + r"""

Shape:
    - Input: :math:`(N, C_{in}, H_{in}, W_{in})` or :math:`(C_{in}, H_{in}, W_{in})`
    - Output: :math:`(N, C_{out}, H_{out}, W_{out})` or :math:`(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: Union[str, _size_2_t] = 0,
    dilation: _size_2_t = 1,
    groups: int = 1,
    bias: bool = True,
    padding_mode: str = "zeros",  # TODO: refine this type
    device=None,
    dtype=None,
) -> None:
    factory_kwargs = {"device": device, "dtype": dtype}
    kernel_size_ = _pair(kernel_size)
    stride_ = _pair(stride)
    padding_ = padding if isinstance(padding, str) else _pair(padding)
    dilation_ = _pair(dilation)
    super().__init__(
        in_channels,
        out_channels,
        kernel_size_,
        stride_,
        padding_,
        dilation_,
        False,
        _pair(0),
        groups,
        bias,
        padding_mode,
        **factory_kwargs,
    )

def _conv_forward(self, input: Tensor, weight: Tensor, bias: Optional[Tensor]):
    if self.padding_mode != "zeros":
        return F.conv2d(
            F.pad(
                input, self._reversed_padding_repeated_twice, mode=self.padding_mode
            ),
            weight,
            bias,
            self.stride,
            _pair(0),
            self.dilation,
            self.groups,
        )
    return F.conv2d(
        input, weight, bias, self.stride, self.padding, self.dilation, self.groups
    )

def forward(self, input: Tensor) -> Tensor:
    return self._conv_forward(input, self.weight, self.bias)

[docs]class Conv3d(_ConvNd): doc = ( 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
"""
    + r"""

This module supports :ref:`TensorFloat32<tf32_on_ampere>`.

On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision<fp16_on_mi200>` for backward.

* :attr:`stride` controls the stride for the cross-correlation.

* :attr:`padding` controls the amount of padding applied to the input. It
  can be either a string {{'valid', 'same'}} or a tuple of ints giving the
  amount of implicit padding applied on both sides.

""" """ * :attr:dilation controls the spacing between the kernel points; also known as the \u00e0 trous algorithm. It is harder to describe, but this link_ has a nice visualization of what :attr:dilation does. """ r"""

{groups_note}

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:
    {depthwise_separable_note}

Note:
    {cudnn_reproducibility_note}

Note:
    ``padding='valid'`` is the same as no padding. ``padding='same'`` pads
    the input so the output has the shape as the input. However, this mode
    doesn't support any stride values other than 1.

Note:
    This module supports complex data types i.e. ``complex32, complex64, complex128``.

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, tuple or str, optional): Padding added to all six 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``
    padding_mode (str, optional): ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. Default: ``'zeros'``
""".format(
        **reproducibility_notes, **convolution_notes
    )
    + r"""

Shape:
    - Input: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})` or :math:`(C_{in}, D_{in}, H_{in}, W_{in})`
    - Output: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})` or :math:`(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: Union[str, _size_3_t] = 0,
    dilation: _size_3_t = 1,
    groups: int = 1,
    bias: bool = True,
    padding_mode: str = "zeros",
    device=None,
    dtype=None,
) -> None:
    factory_kwargs = {"device": device, "dtype": dtype}
    kernel_size_ = _triple(kernel_size)
    stride_ = _triple(stride)
    padding_ = padding if isinstance(padding, str) else _triple(padding)
    dilation_ = _triple(dilation)
    super().__init__(
        in_channels,
        out_channels,
        kernel_size_,
        stride_,
        padding_,
        dilation_,
        False,
        _triple(0),
        groups,
        bias,
        padding_mode,
        **factory_kwargs,
    )

def _conv_forward(self, input: Tensor, weight: Tensor, bias: Optional[Tensor]):
    if self.padding_mode != "zeros":
        return F.conv3d(
            F.pad(
                input, self._reversed_padding_repeated_twice, mode=self.padding_mode
            ),
            weight,
            bias,
            self.stride,
            _triple(0),
            self.dilation,
            self.groups,
        )
    return F.conv3d(
        input, weight, bias, self.stride, self.padding, self.dilation, self.groups
    )

def forward(self, input: Tensor) -> Tensor:
    return self._conv_forward(input, self.weight, self.bias)

class _ConvTransposeNd(_ConvNd): def init( self, in_channels, out_channels, kernel_size, stride, padding, dilation, transposed, output_padding, groups, bias, padding_mode, device=None, dtype=None, ) -> None: if padding_mode != "zeros": raise ValueError( f'Only "zeros" padding mode is supported for {self.class.name}' )

    factory_kwargs = {"device": device, "dtype": dtype}
    super().__init__(
        in_channels,
        out_channels,
        kernel_size,
        stride,
        padding,
        dilation,
        transposed,
        output_padding,
        groups,
        bias,
        padding_mode,
        **factory_kwargs,
    )

# dilation being an optional parameter is for backwards
# compatibility
def _output_padding(
    self,
    input: Tensor,
    output_size: Optional[list[int]],
    stride: list[int],
    padding: list[int],
    kernel_size: list[int],
    num_spatial_dims: int,
    dilation: Optional[list[int]] = None,
) -> list[int]:
    if output_size is None:
        ret = _single(self.output_padding)  # converting to list if was not already
    else:
        has_batch_dim = input.dim() == num_spatial_dims + 2
        num_non_spatial_dims = 2 if has_batch_dim else 1
        if len(output_size) == num_non_spatial_dims + num_spatial_dims:
            output_size = output_size[num_non_spatial_dims:]
        if len(output_size) != num_spatial_dims:
            raise ValueError(
                f"ConvTranspose{num_spatial_dims}D: for {input.dim()}D input, output_size must have {num_spatial_dims} "
                f"or {num_non_spatial_dims + num_spatial_dims} elements (got {len(output_size)})"
            )

        min_sizes = torch.jit.annotate(list[int], [])
        max_sizes = torch.jit.annotate(list[int], [])
        for d in range(num_spatial_dims):
            dim_size = (
                (input.size(d + num_non_spatial_dims) - 1) * stride[d]
                - 2 * padding[d]
                + (dilation[d] if dilation is not None else 1)
                * (kernel_size[d] - 1)
                + 1
            )
            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(
                    f"requested an output size of {output_size}, but valid sizes range "
                    f"from {min_sizes} to {max_sizes} (for an input of {input.size()[2:]})"
                )

        res = torch.jit.annotate(list[int], [])
        for d in range(num_spatial_dims):
            res.append(output_size[d] - min_sizes[d])

        ret = res
    return ret

[docs]class ConvTranspose1d(_ConvTransposeNd): doc = ( 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 as it does
not compute a true inverse of convolution). For more information, see the visualizations
`here`_ and the `Deconvolutional Networks`_ paper.

This module supports :ref:`TensorFloat32<tf32_on_ampere>`.

On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision<fp16_on_mi200>` for backward.

* :attr:`stride` controls the stride for the cross-correlation.

* :attr:`padding` controls the amount of implicit zero padding 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 \u00e0 trous algorithm. It is harder to describe, but the link here_ has a nice visualization of what :attr:dilation does. """ r""" {groups_note}

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
""".format(
        **reproducibility_notes, **convolution_notes
    )
    + r"""

Shape:
    - Input: :math:`(N, C_{in}, L_{in})` or :math:`(C_{in}, L_{in})`
    - Output: :math:`(N, C_{out}, L_{out})` or :math:`(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}}`

.. _`here`:
    https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md

.. _`Deconvolutional Networks`:
    https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf
"""
)

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",
    device=None,
    dtype=None,
) -> None:
    factory_kwargs = {"device": device, "dtype": dtype}
    kernel_size = _single(kernel_size)
    stride = _single(stride)
    padding = _single(padding)
    dilation = _single(dilation)
    output_padding = _single(output_padding)
    super().__init__(
        in_channels,
        out_channels,
        kernel_size,
        stride,
        padding,
        dilation,
        True,
        output_padding,
        groups,
        bias,
        padding_mode,
        **factory_kwargs,
    )

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"
        )

    assert isinstance(self.padding, tuple)
    # One cannot replace List by Tuple or Sequence in "_output_padding" because
    # TorchScript does not support `Sequence[T]` or `Tuple[T, ...]`.
    num_spatial_dims = 1
    output_padding = self._output_padding(
        input,
        output_size,
        self.stride,  # type: ignore[arg-type]
        self.padding,  # type: ignore[arg-type]
        self.kernel_size,  # type: ignore[arg-type]
        num_spatial_dims,
        self.dilation,  # type: ignore[arg-type]
    )
    return F.conv_transpose1d(
        input,
        self.weight,
        self.bias,
        self.stride,
        self.padding,
        output_padding,
        self.groups,
        self.dilation,
    )

[docs]class ConvTranspose2d(_ConvTransposeNd): doc = ( 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 as it does
not compute a true inverse of convolution). For more information, see the visualizations
`here`_ and the `Deconvolutional Networks`_ paper.

This module supports :ref:`TensorFloat32<tf32_on_ampere>`.

On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision<fp16_on_mi200>` for backward.

* :attr:`stride` controls the stride for the cross-correlation.

* :attr:`padding` controls the amount of implicit zero padding 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 \u00e0 trous algorithm. It is harder to describe, but the link here_ has a nice visualization of what :attr:dilation does. """ r""" {groups_note}

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:
    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:
    {cudnn_reproducibility_note}

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
""".format(
        **reproducibility_notes, **convolution_notes
    )
    + r"""

Shape:
    - Input: :math:`(N, C_{in}, H_{in}, W_{in})` or :math:`(C_{in}, H_{in}, W_{in})`
    - Output: :math:`(N, C_{out}, H_{out}, W_{out})` or :math:`(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])

.. _`here`:
    https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md

.. _`Deconvolutional Networks`:
    https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf
"""
)

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: _size_2_t = 1,
    padding_mode: str = "zeros",
    device=None,
    dtype=None,
) -> None:
    factory_kwargs = {"device": device, "dtype": dtype}
    kernel_size = _pair(kernel_size)
    stride = _pair(stride)
    padding = _pair(padding)
    dilation = _pair(dilation)
    output_padding = _pair(output_padding)
    super().__init__(
        in_channels,
        out_channels,
        kernel_size,
        stride,
        padding,
        dilation,
        True,
        output_padding,
        groups,
        bias,
        padding_mode,
        **factory_kwargs,
    )

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"
        )

    assert isinstance(self.padding, tuple)
    # One cannot replace List by Tuple or Sequence in "_output_padding" because
    # TorchScript does not support `Sequence[T]` or `Tuple[T, ...]`.
    num_spatial_dims = 2
    output_padding = self._output_padding(
        input,
        output_size,
        self.stride,  # type: ignore[arg-type]
        self.padding,  # type: ignore[arg-type]
        self.kernel_size,  # type: ignore[arg-type]
        num_spatial_dims,
        self.dilation,  # type: ignore[arg-type]
    )

    return F.conv_transpose2d(
        input,
        self.weight,
        self.bias,
        self.stride,
        self.padding,
        output_padding,
        self.groups,
        self.dilation,
    )

[docs]class ConvTranspose3d(_ConvTransposeNd): doc = ( 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 as it does
not compute a true inverse of convolution). For more information, see the visualizations
`here`_ and the `Deconvolutional Networks`_ paper.

This module supports :ref:`TensorFloat32<tf32_on_ampere>`.

On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision<fp16_on_mi200>` for backward.

* :attr:`stride` controls the stride for the cross-correlation.

* :attr:`padding` controls the amount of implicit zero padding 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 \u00e0 trous algorithm. It is harder to describe, but the link here_ has a nice visualization of what :attr:dilation does. """ r""" {groups_note}

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:
    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:
    {cudnn_reproducibility_note}

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
""".format(
        **reproducibility_notes, **convolution_notes
    )
    + r"""

Shape:
    - Input: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})` or :math:`(C_{in}, D_{in}, H_{in}, W_{in})`
    - Output: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})` or
      :math:`(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)

.. _`here`:
    https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md

.. _`Deconvolutional Networks`:
    https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf
"""
)

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",
    device=None,
    dtype=None,
) -> None:
    factory_kwargs = {"device": device, "dtype": dtype}
    kernel_size = _triple(kernel_size)
    stride = _triple(stride)
    padding = _triple(padding)
    dilation = _triple(dilation)
    output_padding = _triple(output_padding)
    super().__init__(
        in_channels,
        out_channels,
        kernel_size,
        stride,
        padding,
        dilation,
        True,
        output_padding,
        groups,
        bias,
        padding_mode,
        **factory_kwargs,
    )

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"
        )

    assert isinstance(self.padding, tuple)
    # One cannot replace List by Tuple or Sequence in "_output_padding" because
    # TorchScript does not support `Sequence[T]` or `Tuple[T, ...]`.
    num_spatial_dims = 3
    output_padding = self._output_padding(
        input,
        output_size,
        self.stride,  # type: ignore[arg-type]
        self.padding,  # type: ignore[arg-type]
        self.kernel_size,  # type: ignore[arg-type]
        num_spatial_dims,
        self.dilation,  # type: ignore[arg-type]
    )

    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): @deprecated( "_ConvTransposeMixin is a deprecated internal class. " "Please consider using public APIs.", category=FutureWarning, ) def init(self, *args, **kwargs): super().init(*args, **kwargs)

TODO: Conv2dLocal

TODO: Conv2dMap

TODO: ConvTranspose2dMap

class _LazyConvXdMixin(LazyModuleMixin): groups: int transposed: bool in_channels: int out_channels: int kernel_size: tuple[int, ...] weight: UninitializedParameter bias: UninitializedParameter

def reset_parameters(self) -> None:
    # has_uninitialized_params is defined in parent class and it is using a protocol on self
    if not self.has_uninitialized_params() and self.in_channels != 0:  # type: ignore[misc]
        # "type:ignore[..]" is required because mypy thinks that "reset_parameters" is undefined
        # in super class. Turns out that it is defined in _ConvND which is inherited by any class
        # that also inherits _LazyConvXdMixin
        super().reset_parameters()  # type: ignore[misc]

# Signature of "initialize_parameters" is incompatible with the definition in supertype LazyModuleMixin
def initialize_parameters(self, input: Tensor, *args, **kwargs) -> None:  # type: ignore[override]
    # defined by parent class but using a protocol
    if self.has_uninitialized_params():  # type: ignore[misc]
        self.in_channels = self._get_in_channels(input)
        if self.in_channels % self.groups != 0:
            raise ValueError("in_channels must be divisible by groups")
        assert isinstance(self.weight, UninitializedParameter)
        if self.transposed:
            self.weight.materialize(
                (
                    self.in_channels,
                    self.out_channels // self.groups,
                    *self.kernel_size,
                )
            )
        else:
            self.weight.materialize(
                (
                    self.out_channels,
                    self.in_channels // self.groups,
                    *self.kernel_size,
                )
            )
        if self.bias is not None:
            assert isinstance(self.bias, UninitializedParameter)
            self.bias.materialize((self.out_channels,))
        self.reset_parameters()

# Function to extract in_channels from first input.
def _get_in_channels(self, input: Tensor) -> int:
    num_spatial_dims = self._get_num_spatial_dims()
    num_dims_no_batch = num_spatial_dims + 1  # +1 for channels dim
    num_dims_batch = num_dims_no_batch + 1
    if input.dim() not in (num_dims_no_batch, num_dims_batch):
        raise RuntimeError(
            f"Expected {num_dims_no_batch}D (unbatched) or {num_dims_batch}D (batched) input "
            f"to {self.__class__.__name__}, but "
            f"got input of size: {input.shape}"
        )
    return input.shape[1] if input.dim() == num_dims_batch else input.shape[0]

# Function to return the number of spatial dims expected for inputs to the module.
# This is expected to be implemented by subclasses.
def _get_num_spatial_dims(self) -> int:
    raise NotImplementedError

LazyConv1d defines weight as a Tensor but derived class defines it as UnitializeParameter

[docs]class LazyConv1d(_LazyConvXdMixin, Conv1d): # type: ignore[misc] r"""A :class:torch.nn.Conv1d module with lazy initialization of the in_channels argument.

The ``in_channels`` argument of the :class:`Conv1d` is inferred from the ``input.size(1)``.
The attributes that will be lazily initialized are `weight` and `bias`.

Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation
on lazy modules and their limitations.

Args:
    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``
    padding_mode (str, optional): ``'zeros'``, ``'reflect'``,
        ``'replicate'`` or ``'circular'``. Default: ``'zeros'``

.. seealso:: :class:`torch.nn.Conv1d` and :class:`torch.nn.modules.lazy.LazyModuleMixin`
"""

# super class define this variable as None. "type: ignore[..] is required
# since we are redefining the variable.
cls_to_become = Conv1d  # type: ignore[assignment]

def __init__(
    self,
    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",
    device=None,
    dtype=None,
) -> None:
    factory_kwargs = {"device": device, "dtype": dtype}
    super().__init__(
        0,
        0,
        kernel_size,
        stride,
        padding,
        dilation,
        groups,
        # bias is hardcoded to False to avoid creating tensor
        # that will soon be overwritten.
        False,
        padding_mode,
        **factory_kwargs,
    )
    self.weight = UninitializedParameter(**factory_kwargs)
    self.out_channels = out_channels
    if bias:
        self.bias = UninitializedParameter(**factory_kwargs)

def _get_num_spatial_dims(self) -> int:
    return 1

LazyConv2d defines weight as a Tensor but derived class defines it as UnitializeParameter

[docs]class LazyConv2d(_LazyConvXdMixin, Conv2d): # type: ignore[misc] r"""A :class:torch.nn.Conv2d module with lazy initialization of the in_channels argument.

The ``in_channels`` argument of the :class:`Conv2d` that is inferred from the ``input.size(1)``.
The attributes that will be lazily initialized are `weight` and `bias`.

Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation
on lazy modules and their limitations.

Args:
    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``
    padding_mode (str, optional): ``'zeros'``, ``'reflect'``,
        ``'replicate'`` or ``'circular'``. Default: ``'zeros'``

.. seealso:: :class:`torch.nn.Conv2d` and :class:`torch.nn.modules.lazy.LazyModuleMixin`
"""

# super class define this variable as None. "type: ignore[..] is required
# since we are redefining the variable.
cls_to_become = Conv2d  # type: ignore[assignment]

def __init__(
    self,
    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
    device=None,
    dtype=None,
) -> None:
    factory_kwargs = {"device": device, "dtype": dtype}
    super().__init__(
        0,
        0,
        kernel_size,
        stride,
        padding,
        dilation,
        groups,
        # bias is hardcoded to False to avoid creating tensor
        # that will soon be overwritten.
        False,
        padding_mode,
        **factory_kwargs,
    )
    self.weight = UninitializedParameter(**factory_kwargs)
    self.out_channels = out_channels
    if bias:
        self.bias = UninitializedParameter(**factory_kwargs)

def _get_num_spatial_dims(self) -> int:
    return 2

LazyConv3d defines weight as a Tensor but derived class defines it as UnitializeParameter

[docs]class LazyConv3d(_LazyConvXdMixin, Conv3d): # type: ignore[misc] r"""A :class:torch.nn.Conv3d module with lazy initialization of the in_channels argument.

The ``in_channels`` argument of the :class:`Conv3d` that is inferred from
the ``input.size(1)``.
The attributes that will be lazily initialized are `weight` and `bias`.

Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation
on lazy modules and their limitations.

Args:
    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``
    padding_mode (str, optional): ``'zeros'``, ``'reflect'``,
        ``'replicate'`` or ``'circular'``. Default: ``'zeros'``

.. seealso:: :class:`torch.nn.Conv3d` and :class:`torch.nn.modules.lazy.LazyModuleMixin`
"""

# super class define this variable as None. "type: ignore[..] is required
# since we are redefining the variable.
cls_to_become = Conv3d  # type: ignore[assignment]

def __init__(
    self,
    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",
    device=None,
    dtype=None,
) -> None:
    factory_kwargs = {"device": device, "dtype": dtype}
    super().__init__(
        0,
        0,
        kernel_size,
        stride,
        padding,
        dilation,
        groups,
        # bias is hardcoded to False to avoid creating tensor
        # that will soon be overwritten.
        False,
        padding_mode,
        **factory_kwargs,
    )
    self.weight = UninitializedParameter(**factory_kwargs)
    self.out_channels = out_channels
    if bias:
        self.bias = UninitializedParameter(**factory_kwargs)

def _get_num_spatial_dims(self) -> int:
    return 3

LazyConvTranspose1d defines weight as a Tensor but derived class defines it as UnitializeParameter

[docs]class LazyConvTranspose1d(_LazyConvXdMixin, ConvTranspose1d): # type: ignore[misc] r"""A :class:torch.nn.ConvTranspose1d module with lazy initialization of the in_channels argument.

The ``in_channels`` argument of the :class:`ConvTranspose1d` that is inferred from
the ``input.size(1)``.
The attributes that will be lazily initialized are `weight` and `bias`.

Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation
on lazy modules and their limitations.

Args:
    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

.. seealso:: :class:`torch.nn.ConvTranspose1d` and :class:`torch.nn.modules.lazy.LazyModuleMixin`
"""

# super class define this variable as None. "type: ignore[..] is required
# since we are redefining the variable.
cls_to_become = ConvTranspose1d  # type: ignore[assignment]

def __init__(
    self,
    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",
    device=None,
    dtype=None,
) -> None:
    factory_kwargs = {"device": device, "dtype": dtype}
    super().__init__(
        0,
        0,
        kernel_size,
        stride,
        padding,
        output_padding,
        groups,
        # bias is hardcoded to False to avoid creating tensor
        # that will soon be overwritten.
        False,
        dilation,
        padding_mode,
        **factory_kwargs,
    )
    self.weight = UninitializedParameter(**factory_kwargs)
    self.out_channels = out_channels
    if bias:
        self.bias = UninitializedParameter(**factory_kwargs)

def _get_num_spatial_dims(self) -> int:
    return 1

LazyConvTranspose2d defines weight as a Tensor but derived class defines it as UnitializeParameter

[docs]class LazyConvTranspose2d(_LazyConvXdMixin, ConvTranspose2d): # type: ignore[misc] r"""A :class:torch.nn.ConvTranspose2d module with lazy initialization of the in_channels argument.

The ``in_channels`` argument of the :class:`ConvTranspose2d` is inferred from
the ``input.size(1)``.
The attributes that will be lazily initialized are `weight` and `bias`.

Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation
on lazy modules and their limitations.

Args:
    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

.. seealso:: :class:`torch.nn.ConvTranspose2d` and :class:`torch.nn.modules.lazy.LazyModuleMixin`
"""

# super class define this variable as None. "type: ignore[..] is required
# since we are redefining the variable.
cls_to_become = ConvTranspose2d  # type: ignore[assignment]

def __init__(
    self,
    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",
    device=None,
    dtype=None,
) -> None:
    factory_kwargs = {"device": device, "dtype": dtype}
    super().__init__(
        0,
        0,
        kernel_size,
        stride,
        padding,
        output_padding,
        groups,
        # bias is hardcoded to False to avoid creating tensor
        # that will soon be overwritten.
        False,
        dilation,
        padding_mode,
        **factory_kwargs,
    )
    self.weight = UninitializedParameter(**factory_kwargs)
    self.out_channels = out_channels
    if bias:
        self.bias = UninitializedParameter(**factory_kwargs)

def _get_num_spatial_dims(self) -> int:
    return 2

LazyConvTranspose3d defines weight as a Tensor but derived class defines it as UnitializeParameter

[docs]class LazyConvTranspose3d(_LazyConvXdMixin, ConvTranspose3d): # type: ignore[misc] r"""A :class:torch.nn.ConvTranspose3d module with lazy initialization of the in_channels argument.

The ``in_channels`` argument of the :class:`ConvTranspose3d` is inferred from
the ``input.size(1)``.
The attributes that will be lazily initialized are `weight` and `bias`.

Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation
on lazy modules and their limitations.

Args:
    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

.. seealso:: :class:`torch.nn.ConvTranspose3d` and :class:`torch.nn.modules.lazy.LazyModuleMixin`
"""

# super class define this variable as None. "type: ignore[..] is required
# since we are redefining the variable.
cls_to_become = ConvTranspose3d  # type: ignore[assignment]

def __init__(
    self,
    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",
    device=None,
    dtype=None,
) -> None:
    factory_kwargs = {"device": device, "dtype": dtype}
    super().__init__(
        0,
        0,
        kernel_size,
        stride,
        padding,
        output_padding,
        groups,
        # bias is hardcoded to False to avoid creating tensor
        # that will soon be overwritten.
        False,
        dilation,
        padding_mode,
        **factory_kwargs,
    )
    self.weight = UninitializedParameter(**factory_kwargs)
    self.out_channels = out_channels
    if bias:
        self.bias = UninitializedParameter(**factory_kwargs)

def _get_num_spatial_dims(self) -> int:
    return 3