| import torch |
| import torch.nn as nn |
|
|
|
|
| class DoubleConv(nn.Module): |
| def __init__(self, cin, cout): |
| super().__init__() |
| self.net = nn.Sequential( |
| nn.Conv2d(cin, cout, 3, padding=1), nn.BatchNorm2d(cout), nn.ReLU(inplace=True), |
| nn.Conv2d(cout, cout, 3, padding=1), nn.BatchNorm2d(cout), nn.ReLU(inplace=True), |
| ) |
| def forward(self, x): return self.net(x) |
|
|
|
|
|
|
| class UNet(nn.Module): |
| def __init__(self, in_ch=3, out_ch=3, base=48): |
| super().__init__() |
| self.d1 = DoubleConv(in_ch, base) |
| self.d2 = DoubleConv(base, base * 2) |
| self.d3 = DoubleConv(base * 2, base * 4) |
| self.pool = nn.MaxPool2d(2) |
| self.bottleneck = DoubleConv(base * 4, base * 8) |
| self.up3 = nn.ConvTranspose2d(base * 8, base * 4, 2, stride=2) |
| self.u3 = DoubleConv(base * 8, base * 4) |
| self.up2 = nn.ConvTranspose2d(base * 4, base * 2, 2, stride=2) |
| self.u2 = DoubleConv(base * 4, base * 2) |
| self.up1 = nn.ConvTranspose2d(base * 2, base, 2, stride=2) |
| self.u1 = DoubleConv(base * 2, base) |
| self.out = nn.Conv2d(base, out_ch, 1) |
|
|
| def forward(self, x): |
| c1 = self.d1(x) |
| c2 = self.d2(self.pool(c1)) |
| c3 = self.d3(self.pool(c2)) |
| b = self.bottleneck(self.pool(c3)) |
| x = self.u3(torch.cat([self.up3(b), c3], 1)) |
| x = self.u2(torch.cat([self.up2(x), c2], 1)) |
| x = self.u1(torch.cat([self.up1(x), c1], 1)) |
| return self.out(x) |
|
|
