| """ Median Pool |
| Hacked together by / Copyright 2020 Ross Wightman |
| """ |
| import torch.nn as nn |
| import torch.nn.functional as F |
| from .helpers import to_2tuple, to_4tuple |
|
|
|
|
| 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 = to_2tuple(kernel_size) |
| self.stride = to_2tuple(stride) |
| self.padding = to_4tuple(padding) |
| 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): |
| 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 |
|
|
