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49 lines
1.7 KiB
49 lines
1.7 KiB
6 years ago
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import math
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.nn.modules.utils import _pair, _quadruple
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class MedianPool2d(nn.Module):
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""" Median pool (usable as median filter when stride=1) module.
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Args:
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kernel_size: size of pooling kernel, int or 2-tuple
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stride: pool stride, int or 2-tuple
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padding: pool padding, int or 4-tuple (l, r, t, b) as in pytorch F.pad
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same: override padding and enforce same padding, boolean
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"""
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def __init__(self, kernel_size=3, stride=1, padding=0, same=False):
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super(MedianPool2d, self).__init__()
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self.k = _pair(kernel_size)
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self.stride = _pair(stride)
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self.padding = _quadruple(padding) # convert to l, r, t, b
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self.same = same
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def _padding(self, x):
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if self.same:
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ih, iw = x.size()[2:]
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if ih % self.stride[0] == 0:
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ph = max(self.k[0] - self.stride[0], 0)
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else:
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ph = max(self.k[0] - (ih % self.stride[0]), 0)
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if iw % self.stride[1] == 0:
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pw = max(self.k[1] - self.stride[1], 0)
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else:
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pw = max(self.k[1] - (iw % self.stride[1]), 0)
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pl = pw // 2
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pr = pw - pl
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pt = ph // 2
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pb = ph - pt
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padding = (pl, pr, pt, pb)
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else:
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padding = self.padding
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return padding
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def forward(self, x):
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x = F.pad(x, self._padding(x), mode='reflect')
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x = x.unfold(2, self.k[0], self.stride[0]).unfold(3, self.k[1], self.stride[1])
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x = x.contiguous().view(x.size()[:4] + (-1,)).median(dim=-1)[0]
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return x
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