Define and load UNet into the GPU

UNet.py

class DoubleConv2D(nn.Module):
    """(convolution => [BN] => ReLU) * 2"""

    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.double_conv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )

    def forward(self, x):
        return self.double_conv(x)

class Down(nn.Module):
    """Downscaling with maxpool then double conv"""

    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.maxpool_conv = nn.Sequential(
            nn.MaxPool2d(2),
            DoubleConv2D(in_channels, out_channels)
        )

    def forward(self, x):
        return self.maxpool_conv(x)

class Up(nn.Module):
    """Upscaling then double conv"""

    def __init__(self, in_channels, out_channels, bilinear=True):
        super().__init__()

        if bilinear:
            self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
        else:
            self.up = nn.ConvTranspose2d(in_channels // 2, in_channels // 2, kernel_size=2, stride=2)

        self.conv = DoubleConv2D(in_channels, out_channels)

    def forward(self, x1, x2):
        x1 = self.up(x1)
        # input is CHW
        diffY = torch.tensor([x2.size()[2] - x1.size()[2]])
        diffX = torch.tensor([x2.size()[3] - x1.size()[3]])

        x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
                        diffY // 2, diffY - diffY // 2])
        x = torch.cat([x2, x1], dim=1)
        return self.conv(x)

class OutConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(OutConv, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)

    def forward(self, x):
        return self.conv(x)

class UNet2D(nn.Module):
  def __init__(self, n_channels=1, n_classes=27, bilinear=True):
    super(UNet2D, self).__init__()
    self.n_channels = n_channels
    self.n_classes = n_classes
    self.bilinear = bilinear
    self.inc = DoubleConv2D(n_channels, 64)
    self.down1 = Down(64, 128)
    self.down2 = Down(128, 256)
    self.down3 = Down(256, 512)
    self.down4 = Down(512, 512)
    self.up1 = Up(1024, 256, bilinear)
    self.up2 = Up(512, 128, bilinear)
    self.up3 = Up(256, 64, bilinear)
    self.up4 = Up(128, 64, bilinear)
    self.outc = OutConv(64, n_classes)
    self.final = nn.Sigmoid()

  def forward(self, x):
      x1 = self.inc(x)
      x2 = self.down1(x1)
      x3 = self.down2(x2)
      x4 = self.down3(x3)
      x5 = self.down4(x4)
      x = self.up1(x5, x4)
      x = self.up2(x, x3)
      x = self.up3(x, x2)
      x = self.up4(x, x1)
      x = self.outc(x)
      logits = self.final(x)
      return logits
    
use_cuda = torch.cuda.is_available()
model = UNet2D()
if (use_cuda):
    model.cuda()