torch (version 0.1.0)

nn_max_unpool1d: Computes a partial inverse of MaxPool1d.

Description

MaxPool1d is not fully invertible, since the non-maximal values are lost. MaxUnpool1d takes in as input the output of MaxPool1d 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_unpool1d(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 kernel_size by default.

padding

(int or tuple): Padding that was added to the input

Inputs

• input: the input Tensor to invert

• indices: the indices given out by nn_max_pool1d()

• output_size (optional): the targeted output size

Shape

• Input: \((N, C, H_{in})\)

• Output: \((N, C, H_{out})\), where $$ H_{out} = (H_{in} - 1) \times \mbox{stride}[0] - 2 \times \mbox{padding}[0] + \mbox{kernel\_size}[0] $$ or as given by output_size in the call operator

Examples

# NOT RUN {
if (torch_is_installed()) {
pool <- nn_max_pool1d(2, stride=2, return_indices=TRUE)
unpool <- nn_max_unpool1d(2, stride=2)

input <- torch_tensor(array(1:8/1, dim = c(1,1,8)))
out <- pool(input)
unpool(out[[1]], out[[2]])

# Example showcasing the use of output_size
input <- torch_tensor(array(1:8/1, dim = c(1,1,8)))
out <- pool(input)
unpool(out[[1]], out[[2]], output_size=input$size())
unpool(out[[1]], out[[2]])

}
# }