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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, UninitializedParameter
from .. import functional as F
from .. import init
from .lazy import LazyModuleMixin
from .module import Module
from .utils import _single, _pair, _triple, _reverse_repeat_tuple
from torch._torch_docs import reproducibility_notes

from ..common_types import _size_1_t, _size_2_t, _size_3_t
from typing import Optional, List, Tuple

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:
        ...

    _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: Tuple[int, ...],
                 stride: Tuple[int, ...],
                 padding: Tuple[int, ...],
                 dilation: Tuple[int, ...],
                 transposed: bool,
                 output_padding: Tuple[int, ...],
                 groups: int,
                 bias: bool,
                 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): __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>`. * :attr:`stride` controls the stride for the cross-correlation, a single number or a one-element tuple. * :attr:`padding` controls the amount of implicit padding 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. {groups_note} Note: {depthwise_separable_note} 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): 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`` """.format(**reproducibility_notes, **convolution_notes) + r""" 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 ): # 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_ = _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 _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>`. * :attr:`stride` controls the stride for the cross-correlation, a single number or a tuple. * :attr:`padding` controls the amount of implicit padding 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. {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} 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`` """.format(**reproducibility_notes, **convolution_notes) + r""" 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: 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>`. * :attr:`stride` controls the stride for the cross-correlation. * :attr:`padding` controls the amount of implicit padding 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. {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} 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`` """.format(**reproducibility_notes, **convolution_notes) + r""" 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) # 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], 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: 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] + (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(( "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): __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). This module supports :ref:`TensorFloat32<tf32_on_ampere>`. * :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 à trous algorithm. It is harder to describe, but this `link`_ has a nice visualization of what :attr:`dilation` does. {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})` - 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}}` .. _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, 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') # One cannot replace List by Tuple or Sequence in "_output_padding" because # TorchScript does not support `Sequence[T]` or `Tuple[T, ...]`. output_padding = self._output_padding( input, output_size, self.stride, self.padding, self.kernel_size, self.dilation) # type: ignore 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). This module supports :ref:`TensorFloat32<tf32_on_ampere>`. * :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 à trous algorithm. It is harder to describe, but this `link`_ has a nice visualization of what :attr:`dilation` does. {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})` - 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') # One cannot replace List by Tuple or Sequence in "_output_padding" because # TorchScript does not support `Sequence[T]` or `Tuple[T, ...]`. output_padding = self._output_padding( input, output_size, self.stride, self.padding, self.kernel_size, self.dilation) # type: ignore 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). This module supports :ref:`TensorFloat32<tf32_on_ampere>`. * :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 à trous algorithm. It is harder to describe, but this `link`_ has a nice visualization of what :attr:`dilation` does. {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})` - 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') # One cannot replace List by Tuple or Sequence in "_output_padding" because # TorchScript does not support `Sequence[T]` or `Tuple[T, ...]`. output_padding = self._output_padding( input, output_size, self.stride, self.padding, self.kernel_size, self.dilation) # type: ignore 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 class _LazyConvXdMixin(LazyModuleMixin): groups: int transposed: bool in_channels: int out_channels: int kernel_size: Tuple[int, ...] weight: 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) -> None: # type: ignore[override] # defined by parent class but using a protocol if self.has_uninitialized_params(): # type: ignore[misc] self.in_channels = input.shape[1] 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)) self.reset_parameters() # LazyConv1d defines weight as a Tensor but derived class defines it as UnitializeParameter class LazyConv1d(_LazyConvXdMixin, Conv1d): # type: ignore[misc] r"""A :class:`torch.nn.Conv1d` module with lazy initialization of the ``in_channels`` argument of the :class:`Conv1d` that is inferred from the ``input.size(1)``. 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 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`` .. 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' ) -> None: super().__init__( 0, out_channels, kernel_size, stride, padding, dilation, groups, bias, padding_mode ) self.weight = UninitializedParameter() # LazyConv2d defines weight as a Tensor but derived class defines it as UnitializeParameter class LazyConv2d(_LazyConvXdMixin, Conv2d): # type: ignore[misc] r"""A :class:`torch.nn.Conv2d` module with lazy initialization of the ``in_channels`` argument of the :class:`Conv2d` that is inferred from the ``input.size(1)``. 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 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`` .. 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 ) -> None: super().__init__( 0, out_channels, kernel_size, stride, padding, dilation, groups, bias, padding_mode ) self.weight = UninitializedParameter() # LazyConv3d defines weight as a Tensor but derived class defines it as UnitializeParameter class LazyConv3d(_LazyConvXdMixin, Conv3d): # type: ignore[misc] r"""A :class:`torch.nn.Conv3d` module with lazy initialization of the ``in_channels`` argument of the :class:`Conv3d` that is inferred from the ``input.size(1)``. 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 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`` .. 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' ) -> None: super().__init__( 0, out_channels, kernel_size, stride, padding, dilation, groups, bias, padding_mode ) self.weight = UninitializedParameter() # LazyConvTranspose1d defines weight as a Tensor but derived class defines it as UnitializeParameter class LazyConvTranspose1d(_LazyConvXdMixin, ConvTranspose1d): # type: ignore[misc] r"""A :class:`torch.nn.ConvTranspose1d` module with lazy initialization of the ``in_channels`` argument of the :class:`ConvTranspose1d` that is inferred from the ``input.size(1)``. 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' ) -> None: super().__init__( 0, out_channels, kernel_size, stride, padding, output_padding, groups, bias, dilation, padding_mode ) self.weight = UninitializedParameter() # LazyConvTranspose2d defines weight as a Tensor but derived class defines it as UnitializeParameter class LazyConvTranspose2d(_LazyConvXdMixin, ConvTranspose2d): # type: ignore[misc] r"""A :class:`torch.nn.ConvTranspose2d` module with lazy initialization of the ``in_channels`` argument of the :class:`ConvTranspose2d` that is inferred from the ``input.size(1)``. 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' ) -> None: super().__init__( 0, out_channels, kernel_size, stride, padding, output_padding, groups, bias, dilation, padding_mode ) self.weight = UninitializedParameter() # LazyConvTranspose3d defines weight as a Tensor but derived class defines it as UnitializeParameter class LazyConvTranspose3d(_LazyConvXdMixin, ConvTranspose3d): # type: ignore[misc] r"""A :class:`torch.nn.ConvTranspose3d` module with lazy initialization of the ``in_channels`` argument of the :class:`ConvTranspose3d` that is inferred from the ``input.size(1)``. 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' ) -> None: super().__init__( 0, out_channels, kernel_size, stride, padding, output_padding, groups, bias, dilation, padding_mode ) self.weight = UninitializedParameter()

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