Applies a 2D transposed convolution operator over an input image composed of several input planes.

nn_conv_transpose2d( in_channels, out_channels, kernel_size, stride = 1, padding = 0, output_padding = 0, groups = 1, bias = TRUE, dilation = 1, padding_mode = "zeros" )

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): |

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 |

dilation | (int or tuple, optional): Spacing between kernel elements. Default: 1 |

padding_mode | (string, optional): |

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).

`stride`

controls the stride for the cross-correlation.`padding`

controls the amount of implicit zero-paddings on both sides for`dilation * (kernel_size - 1) - padding`

number of points. See note below for details.`output_padding`

controls the additional size added to one side of the output shape. See note below for details.`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`dilation`

does.`groups`

controls the connections between inputs and outputs.`in_channels`

and`out_channels`

must both be divisible by`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=

`in_channels`

, each input channel is convolved with its own set of filters (of size \(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\)).

The parameters `kernel_size`

, `stride`

, `padding`

, `output_padding`

can either be:

a single

`int`

-- in which case the same value is used for the height and width dimensionsa

`tuple`

of two ints -- in which case, the first`int`

is used for the height dimension, and the second`int`

for the width dimension

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.

The `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 nn_conv2d and a nn_conv_transpose2d are initialized with same
parameters, they are inverses of each other in
regard to the input and output shapes. However, when `stride > 1`

,
nn_conv2d maps multiple input shapes to the same output
shape. `output_padding`

is provided to resolve this ambiguity by
effectively increasing the calculated output shape on one side. Note
that `output_padding`

is only used to find output shape, but does
not actually add zero-padding to output.

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`

.

Input: \((N, C_{in}, H_{in}, W_{in})\)

Output: \((N, C_{out}, H_{out}, W_{out})\) where $$ H_{out} = (H_{in} - 1) \times \mbox{stride}[0] - 2 \times \mbox{padding}[0] + \mbox{dilation}[0] \times (\mbox{kernel\_size}[0] - 1) + \mbox{output\_padding}[0] + 1 $$ $$ W_{out} = (W_{in} - 1) \times \mbox{stride}[1] - 2 \times \mbox{padding}[1] + \mbox{dilation}[1] \times (\mbox{kernel\_size}[1] - 1) + \mbox{output\_padding}[1] + 1 $$

weight (Tensor): the learnable weights of the module of shape \((\mbox{in\_channels}, \frac{\mbox{out\_channels}}{\mbox{groups}},\) \(\mbox{kernel\_size[0]}, \mbox{kernel\_size[1]})\). The values of these weights are sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where \(k = \frac{groups}{C_{\mbox{out}} * \prod_{i=0}^{1}\mbox{kernel\_size}[i]}\)

bias (Tensor): the learnable bias of the module of shape (out_channels) If

`bias`

is`True`

, then the values of these weights are sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where \(k = \frac{groups}{C_{\mbox{out}} * \prod_{i=0}^{1}\mbox{kernel\_size}[i]}\)

if (torch_is_installed()) { # With square kernels and equal stride m <- nn_conv_transpose2d(16, 33, 3, stride=2) # non-square kernels and unequal stride and with padding m <- nn_conv_transpose2d(16, 33, c(3, 5), stride=c(2, 1), padding=c(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_conv_transpose2d(16, 16, 3, stride=2, padding=1) h <- downsample(input) h$size() output <- upsample(h, output_size=input$size()) output$size() }#> [1] 1 16 12 12