Shortcuts

Source code for torch.nn.modules.conv

# coding=utf-8
import math
import torch
from torch.nn.parameter import Parameter
from .. import functional as F
from .. import init
from .module import Module
from .utils import _single, _pair, _triple


class _ConvNd(Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride,
                 padding, dilation, transposed, output_padding, groups, bias):
        super(_ConvNd, self).__init__()
        if in_channels % groups != 0:
            raise ValueError('in_channels must be divisible by groups')
        if out_channels % groups != 0:
            raise ValueError('out_channels must be divisible by groups')
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.transposed = transposed
        self.output_padding = output_padding
        self.groups = groups
        if transposed:
            self.weight = Parameter(torch.Tensor(
                in_channels, out_channels // groups, *kernel_size))
        else:
            self.weight = Parameter(torch.Tensor(
                out_channels, in_channels // groups, *kernel_size))
        if bias:
            self.bias = Parameter(torch.Tensor(out_channels))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self):
        n = self.in_channels
        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'
        return s.format(**self.__dict__)


[docs]class Conv1d(_ConvNd): r"""Applies a 1D convolution over an input signal composed of several input planes. In the simplest case, the output value of the layer with input size :math:`(N, C_{\text{in}}, L)` and output :math:`(N, C_{\text{out}}, L_{\text{out}})` can be precisely described as: .. math:: \text{out}(N_i, C_{\text{out}_j}) = \text{bias}(C_{\text{out}_j}) + \sum_{k = 0}^{C_{in} - 1} \text{weight}(C_{\text{out}_j}, k) \star \text{input}(N_i, k) where :math:`\star` is the valid `cross-correlation`_ operator, :math:`N` is a batch size, :math:`C` denotes a number of channels, :math:`L` is a length of signal sequence. * :attr:`stride` controls the stride for the cross-correlation, a single number or a one-element tuple. * :attr:`padding` controls the amount of implicit zero-paddings on both sides for :attr:`padding` number of points. * :attr:`dilation` controls the spacing between the kernel points; also known as the à trous algorithm. It is harder to describe, but this `link`_ has a nice visualization of what :attr:`dilation` does. * :attr:`groups` controls the connections between inputs and outputs. :attr:`in_channels` and :attr:`out_channels` must both be divisible by :attr:`groups`. For example, * At groups=1, all inputs are convolved to all outputs. * At groups=2, the operation becomes equivalent to having two conv layers side by side, each seeing half the input channels, and producing half the output channels, and both subsequently concatenated. * At groups= :attr:`in_channels`, each input channel is convolved with its own set of filters, of size :math:`\left\lfloor\frac{C_\text{out}}{C_\text{in}}\right\rfloor` .. note:: Depending of the size of your kernel, several (of the last) columns of the input might be lost, because it is a valid `cross-correlation`_, and not a full `cross-correlation`_. It is up to the user to add proper padding. .. note:: When `groups == in_channels` and `out_channels == K * in_channels`, where `K` is a positive integer, this operation is also termed in literature as depthwise convolution. In other words, for an input of size :math:`(N, C_{in}, L_{in})`, a depthwise convolution with a depthwise multiplier `K`, can be constructed by arguments :math:`(C_\text{in}=C_{in}, C_\text{out}=C_{in} \times K, ..., \text{groups}=C_{in})`. .. include:: cudnn_deterministic.rst Args: in_channels (int): Number of channels in the input image out_channels (int): Number of channels produced by the convolution kernel_size (int or tuple): Size of the convolving kernel stride (int or tuple, optional): Stride of the convolution. Default: 1 padding (int or tuple, optional): Zero-padding added to both sides of the input. Default: 0 dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True`` Shape: - Input: :math:`(N, C_{in}, L_{in})` - Output: :math:`(N, C_{out}, L_{out})` where .. math:: L_{out} = \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 (out_channels, in_channels, kernel_size). The values of these weights are sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{1}{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{1}{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, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True): kernel_size = _single(kernel_size) stride = _single(stride) padding = _single(padding) dilation = _single(dilation) super(Conv1d, self).__init__( in_channels, out_channels, kernel_size, stride, padding, dilation, False, _single(0), groups, bias) def forward(self, input): return F.conv1d(input, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
[docs]class Conv2d(_ConvNd): r"""Applies a 2D convolution over an input signal composed of several input planes. In the simplest case, the output value of the layer with input size :math:`(N, C_{\text{in}}, H, W)` and output :math:`(N, C_{\text{out}}, H_{\text{out}}, W_{\text{out}})` can be precisely described as: .. math:: \text{out}(N_i, C_{\text{out}_j}) = \text{bias}(C_{\text{out}_j}) + \sum_{k = 0}^{C_{\text{in}} - 1} \text{weight}(C_{\text{out}_j}, k) \star \text{input}(N_i, k) where :math:`\star` is the valid 2D `cross-correlation`_ operator, :math:`N` is a batch size, :math:`C` denotes a number of channels, :math:`H` is a height of input planes in pixels, and :math:`W` is width in pixels. * :attr:`stride` controls the stride for the cross-correlation, a single number or a tuple. * :attr:`padding` controls the amount of implicit zero-paddings on both sides for :attr:`padding` number of points for each dimension. * :attr:`dilation` controls the spacing between the kernel points; also known as the à trous algorithm. It is harder to describe, but this `link`_ has a nice visualization of what :attr:`dilation` does. * :attr:`groups` controls the connections between inputs and outputs. :attr:`in_channels` and :attr:`out_channels` must both be divisible by :attr:`groups`. For example, * At groups=1, all inputs are convolved to all outputs. * At groups=2, the operation becomes equivalent to having two conv layers side by side, each seeing half the input channels, and producing half the output channels, and both subsequently concatenated. * At groups= :attr:`in_channels`, each input channel is convolved with its own set of filters, of size: :math:`\left\lfloor\frac{C_\text{out}}{C_\text{in}}\right\rfloor`. The parameters :attr:`kernel_size`, :attr:`stride`, :attr:`padding`, :attr:`dilation` can either be: - a single ``int`` -- in which case the same value is used for the height and width dimension - a ``tuple`` of two ints -- in which case, the first `int` is used for the height dimension, and the second `int` for the width dimension .. note:: Depending of the size of your kernel, several (of the last) columns of the input might be lost, because it is a valid `cross-correlation`_, and not a full `cross-correlation`_. It is up to the user to add proper padding. .. note:: When `groups == in_channels` and `out_channels == K * in_channels`, where `K` is a positive integer, this operation is also termed in literature as depthwise convolution. In other words, for an input of size :math:`(N, C_{in}, H_{in}, W_{in})`, a depthwise convolution with a depthwise multiplier `K`, can be constructed by arguments :math:`(in\_channels=C_{in}, out\_channels=C_{in} \times K, ..., groups=C_{in})`. .. include:: cudnn_deterministic.rst Args: in_channels (int): Number of channels in the input image out_channels (int): Number of channels produced by the convolution kernel_size (int or tuple): Size of the convolving kernel stride (int or tuple, optional): Stride of the convolution. Default: 1 padding (int or tuple, optional): Zero-padding added to both sides of the input. Default: 0 dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True`` Shape: - Input: :math:`(N, C_{in}, H_{in}, W_{in})` - Output: :math:`(N, C_{out}, H_{out}, W_{out})` where .. math:: H_{out} = \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 (out_channels, in_channels, kernel_size[0], kernel_size[1]). The values of these weights are sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{1}{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{1}{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, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True): kernel_size = _pair(kernel_size) stride = _pair(stride) padding = _pair(padding) dilation = _pair(dilation) super(Conv2d, self).__init__( in_channels, out_channels, kernel_size, stride, padding, dilation, False, _pair(0), groups, bias) def forward(self, input): return F.conv2d(input, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
[docs]class Conv3d(_ConvNd): r"""Applies a 3D convolution over an input signal composed of several input planes. In the simplest case, the output value of the layer with input size :math:`(N, C_{in}, D, H, W)` and output :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})` can be precisely described as: .. math:: out(N_i, C_{out_j}) = bias(C_{out_j}) + \sum_{k = 0}^{C_{in} - 1} weight(C_{out_j}, k) \star input(N_i, k) where :math:`\star` is the valid 3D `cross-correlation`_ operator * :attr:`stride` controls the stride for the cross-correlation. * :attr:`padding` controls the amount of implicit zero-paddings on both sides for :attr:`padding` number of points for each dimension. * :attr:`dilation` controls the spacing between the kernel points; also known as the à trous algorithm. It is harder to describe, but this `link`_ has a nice visualization of what :attr:`dilation` does. * :attr:`groups` controls the connections between inputs and outputs. :attr:`in_channels` and :attr:`out_channels` must both be divisible by :attr:`groups`. For example, * At groups=1, all inputs are convolved to all outputs. * At groups=2, the operation becomes equivalent to having two conv layers side by side, each seeing half the input channels, and producing half the output channels, and both subsequently concatenated. * At groups= :attr:`in_channels`, each input channel is convolved with its own set of filters, of size :math:`\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor`. The parameters :attr:`kernel_size`, :attr:`stride`, :attr:`padding`, :attr:`dilation` can either be: - a single ``int`` -- in which case the same value is used for the depth, height and width dimension - a ``tuple`` of three ints -- in which case, the first `int` is used for the depth dimension, the second `int` for the height dimension and the third `int` for the width dimension .. note:: Depending of the size of your kernel, several (of the last) columns of the input might be lost, because it is a valid `cross-correlation`_, and not a full `cross-correlation`_. It is up to the user to add proper padding. .. note:: When `groups == in_channels` and `out_channels == K * in_channels`, where `K` is a positive integer, this operation is also termed in literature as depthwise convolution. In other words, for an input of size :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`, a depthwise convolution with a depthwise multiplier `K`, can be constructed by arguments :math:`(in\_channels=C_{in}, out\_channels=C_{in} \times K, ..., groups=C_{in})`. .. include:: cudnn_deterministic.rst Args: in_channels (int): Number of channels in the input image out_channels (int): Number of channels produced by the convolution kernel_size (int or tuple): Size of the convolving kernel stride (int or tuple, optional): Stride of the convolution. Default: 1 padding (int or tuple, optional): Zero-padding added to all three sides of the input. Default: 0 dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True`` Shape: - Input: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})` - Output: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})` where .. math:: D_{out} = \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 (out_channels, in_channels, kernel_size[0], kernel_size[1], kernel_size[2]) The values of these weights are sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{1}{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{1}{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, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True): kernel_size = _triple(kernel_size) stride = _triple(stride) padding = _triple(padding) dilation = _triple(dilation) super(Conv3d, self).__init__( in_channels, out_channels, kernel_size, stride, padding, dilation, False, _triple(0), groups, bias) def forward(self, input): return F.conv3d(input, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
class _ConvTransposeMixin(object): def forward(self, input, output_size=None): output_padding = self._output_padding(input, output_size) func = self._backend.ConvNd( self.stride, self.padding, self.dilation, self.transposed, output_padding, self.groups) if self.bias is None: return func(input, self.weight) else: return func(input, self.weight, self.bias) def _output_padding(self, input, output_size): if output_size is None: return self.output_padding output_size = list(output_size) k = input.dim() - 2 if len(output_size) == k + 2: output_size = output_size[2:] if len(output_size) != k: raise ValueError( "output_size must have {} or {} elements (got {})" .format(k, k + 2, len(output_size))) def dim_size(d): return ((input.size(d + 2) - 1) * self.stride[d] - 2 * self.padding[d] + self.kernel_size[d]) min_sizes = [dim_size(d) for d in range(k)] max_sizes = [min_sizes[d] + self.stride[d] - 1 for d in range(k)] for size, min_size, max_size in zip(output_size, min_sizes, max_sizes): if size < min_size or size > max_size: raise ValueError(( "requested an output size of {}, but valid sizes range " "from {} to {} (for an input of {})").format( output_size, min_sizes, max_sizes, input.size()[2:])) return tuple([output_size[d] - min_sizes[d] for d in range(k)])
[docs]class ConvTranspose1d(_ConvTransposeMixin, _ConvNd): r"""Applies a 1D transposed convolution operator over an input image composed of several input planes. This module can be seen as the gradient of Conv1d with respect to its input. It is also known as a fractionally-strided convolution or a deconvolution (although it is not an actual deconvolution operation). * :attr:`stride` controls the stride for the cross-correlation. * :attr:`padding` controls the amount of implicit zero-paddings on both sides for ``kernel_size - 1 - padding`` number of points. See note below for details. * :attr:`output_padding` controls the additional size added to one side of the output shape. See note below for details. * :attr:`dilation` controls the spacing between the kernel points; also known as the à trous algorithm. It is harder to describe, but this `link`_ has a nice visualization of what :attr:`dilation` does. * :attr:`groups` controls the connections between inputs and outputs. :attr:`in_channels` and :attr:`out_channels` must both be divisible by :attr:`groups`. For example, * At groups=1, all inputs are convolved to all outputs. * At groups=2, the operation becomes equivalent to having two conv layers side by side, each seeing half the input channels, and producing half the output channels, and both subsequently concatenated. * At groups= :attr:`in_channels`, each input channel is convolved with its own set of filters (of size :math:`\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor`). .. note:: Depending of the size of your kernel, several (of the last) columns of the input might be lost, because it is a valid `cross-correlation`_, and not a full `cross-correlation`_. It is up to the user to add proper padding. .. note:: The :attr:`padding` argument effectively adds ``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. .. include:: cudnn_deterministic.rst 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): ``kernel_size - 1 - padding`` zero-padding will be added to both sides of the input. Default: 0 output_padding (int or tuple, optional): Additional size added to one side of the output shape. Default: 0 groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True`` dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 Shape: - Input: :math:`(N, C_{in}, L_{in})` - Output: :math:`(N, C_{out}, L_{out})` where .. math:: L_{out} = (L_{in} - 1) \times \text{stride} - 2 \times \text{padding} + \text{kernel\_size} + \text{output\_padding} Attributes: weight (Tensor): the learnable weights of the module of shape (in_channels, out_channels, kernel_size[0], kernel_size[1]). The values of these weights are sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{1}{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{1}{C_\text{in} * \text{kernel\_size}}` """ def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, output_padding=0, groups=1, bias=True, dilation=1): kernel_size = _single(kernel_size) stride = _single(stride) padding = _single(padding) dilation = _single(dilation) output_padding = _single(output_padding) super(ConvTranspose1d, self).__init__( in_channels, out_channels, kernel_size, stride, padding, dilation, True, output_padding, groups, bias) def forward(self, input, output_size=None): output_padding = self._output_padding(input, output_size) return F.conv_transpose1d( input, self.weight, self.bias, self.stride, self.padding, output_padding, self.groups, self.dilation)
[docs]class ConvTranspose2d(_ConvTransposeMixin, _ConvNd): r"""Applies a 2D transposed convolution operator over an input image composed of several input planes. This module can be seen as the gradient of Conv2d with respect to its input. It is also known as a fractionally-strided convolution or a deconvolution (although it is not an actual deconvolution operation). * :attr:`stride` controls the stride for the cross-correlation. * :attr:`padding` controls the amount of implicit zero-paddings on both sides for ``kernel_size - 1 - padding`` number of points. See note below for details. * :attr:`output_padding` controls the additional size added to one side of the output shape. See note below for details. * :attr:`dilation` controls the spacing between the kernel points; also known as the à trous algorithm. It is harder to describe, but this `link`_ has a nice visualization of what :attr:`dilation` does. * :attr:`groups` controls the connections between inputs and outputs. :attr:`in_channels` and :attr:`out_channels` must both be divisible by :attr:`groups`. For example, * At groups=1, all inputs are convolved to all outputs. * At groups=2, the operation becomes equivalent to having two conv layers side by side, each seeing half the input channels, and producing half the output channels, and both subsequently concatenated. * At groups= :attr:`in_channels`, each input channel is convolved with its own set of filters (of size :math:`\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor`). The parameters :attr:`kernel_size`, :attr:`stride`, :attr:`padding`, :attr:`output_padding` can either be: - a single ``int`` -- in which case the same value is used for the height and width dimensions - a ``tuple`` of two ints -- in which case, the first `int` is used for the height dimension, and the second `int` for the width dimension .. note:: Depending of the size of your kernel, several (of the last) columns of the input might be lost, because it is a valid `cross-correlation`_, and not a full `cross-correlation`_. It is up to the user to add proper padding. .. note:: The :attr:`padding` argument effectively adds ``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. .. include:: cudnn_deterministic.rst 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): ``kernel_size - 1 - padding`` zero-padding will be added to both sides of each dimension in the input. Default: 0 output_padding (int or tuple, optional): Additional size added to one side of each dimension in the output shape. Default: 0 groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True`` dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 Shape: - Input: :math:`(N, C_{in}, H_{in}, W_{in})` - Output: :math:`(N, C_{out}, H_{out}, W_{out})` where .. math:: H_{out} = (H_{in} - 1) \times \text{stride}[0] - 2 \times \text{padding}[0] + \text{kernel\_size}[0] + \text{output\_padding}[0] .. math:: W_{out} = (W_{in} - 1) \times \text{stride}[1] - 2 \times \text{padding}[1] + \text{kernel\_size}[1] + \text{output\_padding}[1] Attributes: weight (Tensor): the learnable weights of the module of shape (in_channels, out_channels, kernel_size[0], kernel_size[1]) The values of these weights are sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{1}{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{1}{C_\text{in} * \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, out_channels, kernel_size, stride=1, padding=0, output_padding=0, groups=1, bias=True, dilation=1): kernel_size = _pair(kernel_size) stride = _pair(stride) padding = _pair(padding) dilation = _pair(dilation) output_padding = _pair(output_padding) super(ConvTranspose2d, self).__init__( in_channels, out_channels, kernel_size, stride, padding, dilation, True, output_padding, groups, bias) def forward(self, input, output_size=None): output_padding = self._output_padding(input, output_size) return F.conv_transpose2d( input, self.weight, self.bias, self.stride, self.padding, output_padding, self.groups, self.dilation)
[docs]class ConvTranspose3d(_ConvTransposeMixin, _ConvNd): r"""Applies a 3D transposed convolution operator over an input image composed of several input planes. The transposed convolution operator multiplies each input value element-wise by a learnable kernel, and sums over the outputs from all input feature planes. This module can be seen as the gradient of Conv3d with respect to its input. It is also known as a fractionally-strided convolution or a deconvolution (although it is not an actual deconvolution operation). * :attr:`stride` controls the stride for the cross-correlation. * :attr:`padding` controls the amount of implicit zero-paddings on both sides for ``kernel_size - 1 - padding`` number of points. See note below for details. * :attr:`output_padding` controls the additional size added to one side of the output shape. See note below for details. * :attr:`dilation` controls the spacing between the kernel points; also known as the à trous algorithm. It is harder to describe, but this `link`_ has a nice visualization of what :attr:`dilation` does. * :attr:`groups` controls the connections between inputs and outputs. :attr:`in_channels` and :attr:`out_channels` must both be divisible by :attr:`groups`. For example, * At groups=1, all inputs are convolved to all outputs. * At groups=2, the operation becomes equivalent to having two conv layers side by side, each seeing half the input channels, and producing half the output channels, and both subsequently concatenated. * At groups= :attr:`in_channels`, each input channel is convolved with its own set of filters (of size :math:`\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor`). The parameters :attr:`kernel_size`, :attr:`stride`, :attr:`padding`, :attr:`output_padding` can either be: - a single ``int`` -- in which case the same value is used for the depth, height and width dimensions - a ``tuple`` of three ints -- in which case, the first `int` is used for the depth dimension, the second `int` for the height dimension and the third `int` for the width dimension .. note:: Depending of the size of your kernel, several (of the last) columns of the input might be lost, because it is a valid `cross-correlation`_, and not a full `cross-correlation`_. It is up to the user to add proper padding. .. note:: The :attr:`padding` argument effectively adds ``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. .. include:: cudnn_deterministic.rst 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): ``kernel_size - 1 - padding`` zero-padding will be added to both sides of each dimension in the input. Default: 0 output_padding (int or tuple, optional): Additional size added to one side of each dimension in the output shape. Default: 0 groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True`` dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 Shape: - Input: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})` - Output: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})` where .. math:: D_{out} = (D_{in} - 1) \times \text{stride}[0] - 2 \times \text{padding}[0] + \text{kernel\_size}[0] + \text{output\_padding}[0] .. math:: H_{out} = (H_{in} - 1) \times \text{stride}[1] - 2 \times \text{padding}[1] + \text{kernel\_size}[1] + \text{output\_padding}[1] .. math:: W_{out} = (W_{in} - 1) \times \text{stride}[2] - 2 \times \text{padding}[2] + \text{kernel\_size}[2] + \text{output\_padding}[2] Attributes: weight (Tensor): the learnable weights of the module of shape (in_channels, out_channels, kernel_size[0], kernel_size[1], kernel_size[2]) The values of these weights are sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{1}{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{1}{C_\text{in} * \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, out_channels, kernel_size, stride=1, padding=0, output_padding=0, groups=1, bias=True, dilation=1): kernel_size = _triple(kernel_size) stride = _triple(stride) padding = _triple(padding) dilation = _triple(dilation) output_padding = _triple(output_padding) super(ConvTranspose3d, self).__init__( in_channels, out_channels, kernel_size, stride, padding, dilation, True, output_padding, groups, bias) def forward(self, input, output_size=None): output_padding = self._output_padding(input, output_size) return F.conv_transpose3d( input, self.weight, self.bias, self.stride, self.padding, output_padding, self.groups, self.dilation)
# TODO: Conv2dLocal # TODO: Conv2dMap # TODO: ConvTranspose2dMap

Docs

Access comprehensive developer documentation for PyTorch

View Docs

Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

Resources

Find development resources and get your questions answered

View Resources