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  1. class Discriminator(nn.Module):
  2.     def __init__(self, ngpu):
  3.         super(Discriminator, self).__init__()
  4.         self.ngpu = ngpu
  5.         self.main = nn.Sequential(
  6.             # input is (nc) x 64 x 64
  7.             nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),
  8.             nn.LeakyReLU(0.2, inplace=True),
  9.             # state size. (ndf) x 32 x 32
  10.             nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),
  11.             nn.BatchNorm2d(ndf * 2),
  12.             nn.LeakyReLU(0.2, inplace=True),
  13.             # state size. (ndf*2) x 16 x 16
  14.             nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),
  15.             nn.BatchNorm2d(ndf * 4),
  16.             nn.LeakyReLU(0.2, inplace=True),
  17.             # state size. (ndf*4) x 8 x 8
  18.             nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),
  19.             nn.BatchNorm2d(ndf * 8),
  20.             nn.LeakyReLU(0.2, inplace=True),
  21.             # state size. (ndf*8) x 4 x 4
  22.             nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),
  23.             nn.Sigmoid()
  24.         )
  25.  
  26.     def forward(self, input):
  27.         return self.main(input)
  28.      
  29. class Generator(nn.Module):
  30.     def __init__(self, ngpu):
  31.         super(Generator, self).__init__()
  32.         self.ngpu = ngpu
  33.         self.main = nn.Sequential(
  34.             # input is Z, going into a convolution
  35.             nn.ConvTranspose2d( nz, ngf * 8, 4, 1, 0, bias=False),
  36.             nn.BatchNorm2d(ngf * 8),
  37.             nn.ReLU(True),
  38.             # state size. (ngf*8) x 4 x 4
  39.             nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
  40.             nn.BatchNorm2d(ngf * 4),
  41.             nn.ReLU(True),
  42.             # state size. (ngf*4) x 8 x 8
  43.             nn.ConvTranspose2d( ngf * 4, ngf * 2, 4, 2, 1, bias=False),
  44.             nn.BatchNorm2d(ngf * 2),
  45.             nn.ReLU(True),
  46.             # state size. (ngf*2) x 16 x 16
  47.             nn.ConvTranspose2d( ngf * 2, ngf, 4, 2, 1, bias=False),
  48.             nn.BatchNorm2d(ngf),
  49.             nn.ReLU(True),
  50.             # state size. (ngf) x 32 x 32
  51.             nn.ConvTranspose2d( ngf, nc, 4, 2, 1, bias=False),
  52.             nn.Tanh()
  53.             # state size. (nc) x 64 x 64
  54.         )
  55.  
  56.     def forward(self, input):
  57.         return self.main(input)
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