import torch import torch.nn as nn import torch.nn.functional as F class UNet(nn.Module): def __init__(self, in_channels, out_classes): super(UNet, self).__init__() self.inc = inconv(in_channels, 64) self.down1 = down(64, 128) self.down2 = down(128, 256) self.down3 = down(256, 512) self.down4 = down(512, 1024) self.down5 = down(1024, 1024) self.up1 = up(2048, 512) self.up2 = up(1024, 256) self.up3 = up(512, 128) self.up4 = up(256, 64) self.up5 = up(128, 64) self.outc = outconv(64, out_classes) def forward(self, x): x1 = self.inc(x) x2 = self.down1(x1) x3 = self.down2(x2) x4 = self.down3(x3) x5 = self.down4(x4) x6 = self.down5(x5) x = self.up1(x6, x5) x = self.up2(x, x4) x = self.up3(x, x3) x = self.up4(x, x2) x = self.up5(x, x1) x = self.outc(x) return torch.sigmoid(x) class double_conv(nn.Module): '''(conv => BN => ReLU) * 2''' 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__() # would be a nice idea if the upsampling could be learned too, # but my machine do not have enough memory to handle all those weights if bilinear: self.up = nn.Upsample(scale_factor=2.0, mode='bilinear', align_corners=True) else: self.up = nn.ConvTranspose2d(in_ch//2, in_ch//2, 2, stride=2) self.conv = double_conv(in_ch, out_ch) def forward(self, x1, x2): x1 = self.up(x1) # input is CHW diffY = x2.size()[2] - x1.size()[2] diffX = x2.size()[3] - x1.size()[3] x1 = F.pad(x1, (diffX // 2, diffX - diffX//2, diffY // 2, diffY - diffY//2)) # for padding issues, see # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd 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