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model.py
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model.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
class double_conv(nn.Module):
def __init__(self, in_ch, out_ch):
super(double_conv, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_ch, out_ch, 3, padding=1),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True),
nn.Conv2d(out_ch, out_ch, 3, padding=1),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True)
)
def forward(self, x):
x = self.conv(x)
return x
class inconv(nn.Module):
def __init__(self, in_ch, out_ch):
super(inconv, self).__init__()
self.conv = double_conv(in_ch, out_ch)
def forward(self, x):
x = self.conv(x)
return x
class down(nn.Module):
def __init__(self, in_ch, out_ch):
super(down, self).__init__()
self.mpconv = nn.Sequential(
nn.MaxPool2d(2),
double_conv(in_ch, out_ch)
)
def forward(self, x):
x = self.mpconv(x)
return x
class up(nn.Module):
def __init__(self, in_ch, out_ch, bilinear=True):
super(up, self).__init__()
if bilinear:
self.up = nn.UpsamplingBilinear2d(scale_factor=2)
else:
self.up = nn.ConvTranspose2d(in_ch, out_ch, 2, stride=2)
self.conv = double_conv(in_ch, out_ch)
def forward(self, x1, x2):
x1 = self.up(x1)
diffX = x1.size()[2] - x2.size()[2]
diffY = x1.size()[3] - x2.size()[3]
x2 = F.pad(x2, (diffX // 2, int(diffX / 2),
diffY // 2, int(diffY / 2)))
x = torch.cat([x2, x1], dim=1)
x = self.conv(x)
return x
class outconv(nn.Module):
def __init__(self, in_ch, out_ch):
super(outconv, self).__init__()
self.conv = nn.Conv2d(in_ch, out_ch, 1)
def forward(self, x):
x = self.conv(x)
return x
class UNet(nn.Module):
def __init__(self, n_channels, shrink=1):
super(UNet, self).__init__()
self.inc = inconv(n_channels, 64 // shrink)
self.down1 = down(64 // shrink, 128 // shrink)
self.down2 = down(128 // shrink, 256 // shrink)
self.down3 = down(256 // shrink, 512 // shrink)
self.down4 = down(512 // shrink, 512 // shrink)
self.up1 = up(1024 // shrink, 256 // shrink)
self.up2 = up(512 // shrink, 128 // shrink)
self.up3 = up(256 // shrink, 64 // shrink)
self.up4 = up(128 // shrink, 8)
def forward(self, x):
x1 = self.inc(x)
x2 = self.down1(x1)
x3 = self.down2(x2)
x4 = self.down3(x3)
x5 = self.down4(x4)
out1 = self.up1(x5, x4)
out2 = self.up2(out1, x3)
out3 = self.up3(out2, x2)
out4 = self.up4(out3, x1)
return out4