nn_conv_transpose2d: ConvTranpose2D module

nn_conv_transpose2dR Documentation

ConvTranpose2D module


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


  stride = 1,
  padding = 0,
  output_padding = 0,
  groups = 1,
  bias = TRUE,
  dilation = 1,
  padding_mode = "zeros"



(int): Number of channels in the input image


(int): Number of channels produced by the convolution


(int or tuple): Size of the convolving kernel


(int or tuple, optional): Stride of the convolution. Default: 1


(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


(int or tuple, optional): Additional size added to one side of each dimension in the output shape. Default: 0


(int, optional): Number of blocked connections from input channels to output channels. Default: 1


(bool, optional): If True, adds a learnable bias to the output. Default: True


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


(string, optional): 'zeros', 'reflect', 'replicate' or 'circular'. Default: 'zeros'


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 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


  • 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]}


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.


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)
output <- upsample(h, output_size = input$size())

torch documentation built on June 7, 2023, 6:19 p.m.