PyTorch MedianPool (MedianFilter)

median_pool.py

import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.utils import _pair, _quadruple


class MedianPool2d(nn.Module):
    """ Median pool (usable as median filter when stride=1) module.
    
    Args:
         kernel_size: size of pooling kernel, int or 2-tuple
         stride: pool stride, int or 2-tuple
         padding: pool padding, int or 4-tuple (l, r, t, b) as in pytorch F.pad
         same: override padding and enforce same padding, boolean
    """
    def __init__(self, kernel_size=3, stride=1, padding=0, same=False):
        super(MedianPool2d, self).__init__()
        self.k = _pair(kernel_size)
        self.stride = _pair(stride)
        self.padding = _quadruple(padding)  # convert to l, r, t, b
        self.same = same

    def _padding(self, x):
        if self.same:
            ih, iw = x.size()[2:]
            if ih % self.stride[0] == 0:
                ph = max(self.k[0] - self.stride[0], 0)
            else:
                ph = max(self.k[0] - (ih % self.stride[0]), 0)
            if iw % self.stride[1] == 0:
                pw = max(self.k[1] - self.stride[1], 0)
            else:
                pw = max(self.k[1] - (iw % self.stride[1]), 0)
            pl = pw // 2
            pr = pw - pl
            pt = ph // 2
            pb = ph - pt
            padding = (pl, pr, pt, pb)
        else:
            padding = self.padding
        return padding
    
    def forward(self, x):
        # using existing pytorch functions and tensor ops so that we get autograd, 
        # would likely be more efficient to implement from scratch at C/Cuda level
        x = F.pad(x, self._padding(x), mode='reflect')
        x = x.unfold(2, self.k[0], self.stride[0]).unfold(3, self.k[1], self.stride[1])
        x = x.contiguous().view(x.size()[:4] + (-1,)).median(dim=-1)[0]
        return x

In any means, unfold will not help you with any convolutional- or pooling-like operations if the stride is small (e.g., 1), as it will expand the raw data when you start calculation. The only way is to implement a CUDA extension to torch yourself, which is not very hard with the help of the official implementations of ops like max_pooling. It is very inconvenient though.

For the reference of any future people looking into this, here is a slightly different implementation from Korina:
https://kornia.readthedocs.io/en/latest/_modules/kornia/filters/median.html
From what I understand, the main difference seems to be that they are using a convolution to do the unfolding instead. In my rough test, it seems both methods currently have roughly the same performance in terms of speed and memory