Skip to contents

Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data. The input data is assumed to be of the form minibatch x channels x optional depth x optional height] x width. Hence, for spatial inputs, we expect a 4D Tensor and for volumetric inputs, we expect a 5D Tensor.


  size = NULL,
  scale_factor = NULL,
  mode = "nearest",
  align_corners = NULL



(int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int], optional): output spatial sizes


(float or Tuple[float] or Tuple[float, float] or Tuple[float, float, float], optional): multiplier for spatial size. Has to match input size if it is a tuple.


(str, optional): the upsampling algorithm: one of 'nearest', 'linear', 'bilinear', 'bicubic' and 'trilinear'. Default: 'nearest'


(bool, optional): if TRUE, the corner pixels of the input and output tensors are aligned, and thus preserving the values at those pixels. This only has effect when mode is 'linear', 'bilinear', or 'trilinear'. Default: FALSE


The algorithms available for upsampling are nearest neighbor and linear, bilinear, bicubic and trilinear for 3D, 4D and 5D input Tensor, respectively.

One can either give a scale_factor or the target output size to calculate the output size. (You cannot give both, as it is ambiguous)


if (torch_is_installed()) {
input <- torch_arange(start = 1, end = 4, dtype = torch_float())$view(c(1, 1, 2, 2))
nn_upsample(scale_factor = c(2), mode = "nearest")(input)
nn_upsample(scale_factor = c(2, 2), mode = "nearest")(input)
#> torch_tensor
#> (1,1,.,.) = 
#>   1  1  2  2
#>   1  1  2  2
#>   3  3  4  4
#>   3  3  4  4
#> [ CPUFloatType{1,1,4,4} ]