Having the output of MaxUnpool2d to be computed as follows is error-prone.

out_height = (input.size(2) - 1) * self.dh + self.kh - 2*self.padh
out_width = (input.size(3) - 1) * self.dw + self.kw - 2*self.padw

For some configurations of input size and stride/kernel size, there are pixel loss in the boundaries due to integer division, so when unpooling we might end up by having accesses which are out of the output size defined by the above equation, or giving completely wrong results.
Here is a test case:

import torch
import torch.nn as nn
import torch.autograd as autograd

m = nn.MaxPool2d(3, stride=2, return_indices = True)
mu = nn.MaxUnpool2d(3, stride=2)
input_tensor = torch.rand(1, 1, 6, 6)
input_tensor[0][0][5][5] = 2
input = autograd.Variable(input_tensor)
output, indices = m.forward(input)
unpooled_output = mu.forward(output, indices)

Curiously, the segfault is doesn't happen all the time, but instead it just raises an out-of-range error, or the outputs max are not in the right position.