nn_max_unpool2d: Computes a partial inverse of 'MaxPool2d'.

In torch: Tensors and Neural Networks with 'GPU' Acceleration

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 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_pool2d()

• output_size (optional): the targeted output size

Shape

• Input: (N, C, H_{in}, W_{in})

• Output: (N, C, H_{out}, W_{out}), where

  H_{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))
}

torch documentation built on May 29, 2024, 9:54 a.m.