File size: 2,569 Bytes
43fb226 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
from torch import nn
class Generator(nn.Module):
def __init__(self, config):
super(Generator, self).__init__()
self.latent_dim = config["latent_dim"]
self.ngf = config["ngf"]
self.nc = config["nc"]
# DCGAN generator architecture
self.main = nn.Sequential(
# Input: latent vector Z (batch_size, latent_dim, 1, 1)
nn.ConvTranspose2d(self.latent_dim, self.ngf * 8, 4, 1, 0, bias=False),
nn.BatchNorm2d(self.ngf * 8),
nn.ReLU(True),
# State: (ngf*8) x 4 x 4
nn.ConvTranspose2d(self.ngf * 8, self.ngf * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(self.ngf * 4),
nn.ReLU(True),
# State: (ngf*4) x 8 x 8
nn.ConvTranspose2d(self.ngf * 4, self.ngf * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(self.ngf * 2),
nn.ReLU(True),
# State: (ngf*2) x 16 x 16
nn.ConvTranspose2d(self.ngf * 2, self.ngf, 4, 2, 1, bias=False),
nn.BatchNorm2d(self.ngf),
nn.ReLU(True),
# State: (ngf) x 32 x 32
nn.ConvTranspose2d(self.ngf, self.nc, 4, 2, 1, bias=False),
nn.Tanh()
# Output: (nc) x 64 x 64
)
def forward(self, input):
return self.main(input)
class Discriminator(nn.Module):
def __init__(self, config):
super(Discriminator, self).__init__()
self.ndf = config["ndf"]
self.nc = config["nc"]
# DCGAN discriminator architecture
self.main = nn.Sequential(
# Input: (nc) x 64 x 64
nn.Conv2d(self.nc, self.ndf, 4, 2, 1, bias=False),
nn.LeakyReLU(0.2, inplace=True),
# State: (ndf) x 32 x 32
nn.Conv2d(self.ndf, self.ndf * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(self.ndf * 2),
nn.LeakyReLU(0.2, inplace=True),
# State: (ndf*2) x 16 x 16
nn.Conv2d(self.ndf * 2, self.ndf * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(self.ndf * 4),
nn.LeakyReLU(0.2, inplace=True),
# State: (ndf*4) x 8 x 8
nn.Conv2d(self.ndf * 4, self.ndf * 8, 4, 2, 1, bias=False),
nn.BatchNorm2d(self.ndf * 8),
nn.LeakyReLU(0.2, inplace=True),
# State: (ndf*8) x 4 x 4
nn.Conv2d(self.ndf * 8, 1, 4, 1, 0, bias=False),
nn.Sigmoid()
)
def forward(self, input):
return self.main(input).view(-1, 1).squeeze(1) |