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import torch
import torch.nn as nn
import torch.nn.functional as F
class Erosion2d(nn.Module):
def __init__(self, m=1):
super(Erosion2d, self).__init__()
self.m = m
self.pad = [m, m, m, m]
self.unfold = nn.Unfold(2 * m + 1, padding=0, stride=1)
def forward(self, x):
batch_size, c, h, w = x.shape
x_pad = F.pad(x, pad=self.pad, mode='constant', value=1e9)
for i in range(c):
channel = self.unfold(x_pad[:, [i], :, :])
channel = torch.min(channel, dim=1, keepdim=True)[0]
channel = channel.view([batch_size, 1, h, w])
x[:, [i], :, :] = channel
return x
class Dilation2d(nn.Module):
def __init__(self, m=1):
super(Dilation2d, self).__init__()
self.m = m
self.pad = [m, m, m, m]
self.unfold = nn.Unfold(2 * m + 1, padding=0, stride=1)
def forward(self, x):
batch_size, c, h, w = x.shape
x_pad = F.pad(x, pad=self.pad, mode='constant', value=-1e9)
for i in range(c):
channel = self.unfold(x_pad[:, [i], :, :])
channel = torch.max(channel, dim=1, keepdim=True)[0]
channel = channel.view([batch_size, 1, h, w])
x[:, [i], :, :] = channel
return x
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