@randinoo. Hmm I think there are many things that need to be changed. First of all, I think DoubleConv is fine.
As I guess, Down is for reducing the resolution using MaxPool  and doing DoubleConv after that. So I think that class is fine too.
I think Up class needs some modifications.
class Up(nn.Module):
    """Upscaling then double conv"""
    def __init__(self, in_channels, out_channels, bilinear=True):
        super(Up,self).__init__()
        if bilinear:
            self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
            self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
        else:
            self.up = nn.ConvTranspose2d(in_channels , in_channels // 2, kernel_size=2, stride=2)
            self.conv = DoubleConv(in_channels, out_channels)  


    def forward(self, x1, x2):
        x1 = self.up(x1)
        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])
        x = torch.cat([x2, x1], dim=1)#  
        return self.conv(x)

I do not know what are you doing with diffY and diffX but let’s consider those as the right ones.
I think OutConv is good too. Now for the final UNet Class
class UNet(nn.Module):
    def __init__(self):
        super(UNet, self).__init__()
        self.n_channels = 1
        self.n_classes = 2
        self.bilinear = True 
        self.inc = DoubleConv(1, 64)
        self.down1 = Down(64, 128)
        self.down2 = Down(128, 256)
        self.down3 = Down(256, 512)
        factor = 2 if True else 1
        self.down4 = Down(512, 1024 // factor)
        self.up1 = Up(1024, 512 // factor, bilinear)
        self.up2 = Up(512, 256 // factor, bilinear)
        self.up3 = Up(256, 128 // factor, bilinear)
        self.up4 = Up(128, 64, bilinear)
        self.outc = OutConv(64, n_classes)

    def forward(self, x):
         x = self.inc(x)
         x = self.down1(x)
         x = self.down2(x)
         x = self.down3(x)
         x = self.down4(x)
         x = self.up1(x)
         x = self.up2(x)
         x = self.up3(x)
         x = self.up4(x)
         x = self.outc(x)
         return x

I have just started coding on pytorch too but I was working on UNet recently and I think that should be the right classes. I hope it helps you.
Good Luck