Desciminator

nc=3
ndf=64
class Discriminator(nn.Module):  
    def __init__(self):
        super(Discriminator, self).__init__()
        self.main = nn.Sequential(
            # input is (nc) x 128 x 128
            nn.Conv2d(nc,ndf,4,2,1, bias=False),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. (ndf) x 64 x 64
            nn.Conv2d(ndf,ndf*2,4,2,1, bias=False),
            nn.InstanceNorm2d(ndf * 2),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. (ndf*2) x 32 x 32
            nn.Conv2d(ndf*2, ndf * 4, 4, 2, 1, bias=False),
            nn.InstanceNorm2d(ndf * 4),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. (ndf*4) x 16 x 16
            nn.Conv2d(ndf*4,ndf*8,4,1,1),
            nn.InstanceNorm2d(ndf*8),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. (ndf*8) x 15 x 15
            nn.Conv2d(ndf*8,1,4,1,1)
            # state size. 1 x 14 x 14
        )

    def forward(self, input):
        return self.main(input)