nn_max_unpool2d {torch} | R Documentation |
Computes a partial inverse of MaxPool2d
.
Description
MaxPool2d
is not fully invertible, since the non-maximal values are lost.
MaxUnpool2d
takes in as input the output of MaxPool2d
including the indices of the maximal values and computes a partial inverse
in which all non-maximal values are set to zero.
Usage
nn_max_unpool2d(kernel_size, stride = NULL, padding = 0)
Arguments
kernel_size |
(int or tuple): Size of the max pooling window. |
stride |
(int or tuple): Stride of the max pooling window.
It is set to |
padding |
(int or tuple): Padding that was added to the input |
Inputs
-
input
: the input Tensor to invert -
indices
: the indices given out bynn_max_pool2d()
-
output_size
(optional): the targeted output size
Shape
Input:
(N, C, H_{in}, W_{in})
Output:
(N, C, H_{out}, W_{out})
, whereH_{out} = (H_{in} - 1) \times \mbox{stride[0]} - 2 \times \mbox{padding[0]} + \mbox{kernel\_size[0]}
W_{out} = (W_{in} - 1) \times \mbox{stride[1]} - 2 \times \mbox{padding[1]} + \mbox{kernel\_size[1]}
or as given by
output_size
in the call operator
Note
MaxPool2d
can map several input sizes to the same output
sizes. Hence, the inversion process can get ambiguous.
To accommodate this, you can provide the needed output size
as an additional argument output_size
in the forward call.
See the Inputs and Example below.
Examples
if (torch_is_installed()) {
pool <- nn_max_pool2d(2, stride = 2, return_indices = TRUE)
unpool <- nn_max_unpool2d(2, stride = 2)
input <- torch_randn(1, 1, 4, 4)
out <- pool(input)
unpool(out[[1]], out[[2]])
# specify a different output size than input size
unpool(out[[1]], out[[2]], output_size = c(1, 1, 5, 5))
}