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# Kaiyu Li
# https://github.com/likyoo
#
import torch.nn as nn
import torch
class conv_block_nested(nn.Module):
def __init__(self, in_ch, mid_ch, out_ch):
super(conv_block_nested, self).__init__()
self.activation = nn.ReLU(inplace=True)
self.conv1 = nn.Conv2d(in_ch, mid_ch, kernel_size=3, padding=1, bias=True)
self.bn1 = nn.BatchNorm2d(mid_ch)
self.conv2 = nn.Conv2d(mid_ch, out_ch, kernel_size=3, padding=1, bias=True)
self.bn2 = nn.BatchNorm2d(out_ch)
def forward(self, x):
x = self.conv1(x)
identity = x
x = self.bn1(x)
x = self.activation(x)
x = self.conv2(x)
x = self.bn2(x)
output = self.activation(x + identity)
return output
class up(nn.Module):
def __init__(self, in_ch, bilinear=False):
super(up, self).__init__()
if bilinear:
self.up = nn.Upsample(scale_factor=2,
mode='bilinear',
align_corners=True)
else:
self.up = nn.ConvTranspose2d(in_ch, in_ch, 2, stride=2)
def forward(self, x):
x = self.up(x)
return x
class UNetpp(nn.Module):
# SNUNet-CD with ECAM
def __init__(self, in_ch=3):
super(UNetpp, self).__init__()
torch.nn.Module.dump_patches = True
n1 = 32 # the initial number of channels of feature map
filters = [n1, n1 * 2, n1 * 4, n1 * 8, n1 * 16]
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv0_0 = conv_block_nested(in_ch, filters[0], filters[0])
self.conv1_0 = conv_block_nested(filters[0], filters[1], filters[1])
self.Up1_0 = up(filters[1])
self.conv2_0 = conv_block_nested(filters[1], filters[2], filters[2])
self.Up2_0 = up(filters[2])
self.conv3_0 = conv_block_nested(filters[2], filters[3], filters[3])
self.Up3_0 = up(filters[3])
self.conv4_0 = conv_block_nested(filters[3], filters[4], filters[4])
self.Up4_0 = up(filters[4])
self.conv0_1 = conv_block_nested(filters[0] * 2 + filters[1], filters[0], filters[0])
self.conv1_1 = conv_block_nested(filters[1] * 2 + filters[2], filters[1], filters[1])
self.Up1_1 = up(filters[1])
self.conv2_1 = conv_block_nested(filters[2] * 2 + filters[3], filters[2], filters[2])
self.Up2_1 = up(filters[2])
self.conv3_1 = conv_block_nested(filters[3] * 2 + filters[4], filters[3], filters[3])
self.Up3_1 = up(filters[3])
self.conv0_2 = conv_block_nested(filters[0] * 3 + filters[1], filters[0], filters[0])
self.conv1_2 = conv_block_nested(filters[1] * 3 + filters[2], filters[1], filters[1])
self.Up1_2 = up(filters[1])
self.conv2_2 = conv_block_nested(filters[2] * 3 + filters[3], filters[2], filters[2])
self.Up2_2 = up(filters[2])
self.conv0_3 = conv_block_nested(filters[0] * 4 + filters[1], filters[0], filters[0])
self.conv1_3 = conv_block_nested(filters[1] * 4 + filters[2], filters[1], filters[1])
self.Up1_3 = up(filters[1])
self.conv0_4 = conv_block_nested(filters[0] * 5 + filters[1], filters[0], filters[0])
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def forward(self, xA, xB):
'''xA'''
x0_0A = self.conv0_0(xA)
x1_0A = self.conv1_0(self.pool(x0_0A))
x2_0A = self.conv2_0(self.pool(x1_0A))
x3_0A = self.conv3_0(self.pool(x2_0A))
# x4_0A = self.conv4_0(self.pool(x3_0A))
'''xB'''
x0_0B = self.conv0_0(xB)
x1_0B = self.conv1_0(self.pool(x0_0B))
x2_0B = self.conv2_0(self.pool(x1_0B))
x3_0B = self.conv3_0(self.pool(x2_0B))
x4_0B = self.conv4_0(self.pool(x3_0B))
x0_1 = self.conv0_1(torch.cat([x0_0A, x0_0B, self.Up1_0(x1_0B)], 1))
x1_1 = self.conv1_1(torch.cat([x1_0A, x1_0B, self.Up2_0(x2_0B)], 1))
x0_2 = self.conv0_2(torch.cat([x0_0A, x0_0B, x0_1, self.Up1_1(x1_1)], 1))
x2_1 = self.conv2_1(torch.cat([x2_0A, x2_0B, self.Up3_0(x3_0B)], 1))
x1_2 = self.conv1_2(torch.cat([x1_0A, x1_0B, x1_1, self.Up2_1(x2_1)], 1))
x0_3 = self.conv0_3(torch.cat([x0_0A, x0_0B, x0_1, x0_2, self.Up1_2(x1_2)], 1))
x3_1 = self.conv3_1(torch.cat([x3_0A, x3_0B, self.Up4_0(x4_0B)], 1))
x2_2 = self.conv2_2(torch.cat([x2_0A, x2_0B, x2_1, self.Up3_1(x3_1)], 1))
x1_3 = self.conv1_3(torch.cat([x1_0A, x1_0B, x1_1, x1_2, self.Up2_2(x2_2)], 1))
x0_4 = self.conv0_4(torch.cat([x0_0A, x0_0B, x0_1, x0_2, x0_3, self.Up1_3(x1_3)], 1))
return [x0_1, x0_2, x0_3, x0_4]
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