nn_conv_transpose1d function

ConvTranspose1D

Applies a 1D transposed convolution operator over an input image composed of several input planes.

nn_conv_transpose1d(
  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 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
• padding_mode: (string, optional): 'zeros', 'reflect', 'replicate' or 'circular'. Default: 'zeros'

Details

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).

• 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 $\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor$).

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 ~torch.nn.Conv1d and a ~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, ~torch.nn.Conv1d 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: $(N, C_{in}, L_{in})$
• Output: $(N, C_{out}, L_{out})$ where

Attributes

• weight (Tensor): the learnable weights of the module of shape $(\mbox{in\_channels}, \frac{\mbox{out\_channels}}{\mbox{groups}},$

  $\mbox{kernel\_size})$. The values of these weights are sampled from $\mathcal{U}(-\sqrt{k}, \sqrt{k})$ where $k = \frac{groups}{C_{\mbox{out}} * \mbox{kernel\_size}}$

• 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 $k = \frac{groups}{C_{\mbox{out}} * \mbox{kernel\_size}}$

Examples

if (torch_is_installed()) {
m <- nn_conv_transpose1d(32, 16, 2)
input <- torch_randn(10, 32, 2)
output <- m(input)
}