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

## Usage

```
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"
)
```

## Arguments

- 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

- padding_mode
(string, optional):

`'zeros'`

,`'reflect'`

,`'replicate'`

or`'circular'`

. Default:`'zeros'`

## Details

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

## 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.

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`

.

## Shape

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

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

## Attributes

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 \(k = \frac{groups}{C_{\mbox{out}} * \prod_{i=0}^{1}\mbox{kernel\_size}[i]}\)

## Examples

```
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
```