def __init__(self):
super(_netD, self).__init__()
ndf = 64
self.main = nn.Sequential(
# state size. 1 x 32 x 32
SNConv2d(1, ndf, 3, 1, 1, bias=True),
nn.LeakyReLU(0.1, inplace=True),
SNConv2d(ndf, ndf, 4, 2, 1, bias=True),
nn.LeakyReLU(0.1, inplace=True),
# state size. (ndf) x 16 x 16
SNConv2d(ndf, ndf * 2, 3, 1, 1, bias=True),
nn.LeakyReLU(0.1, inplace=True),
SNConv2d(ndf * 2, ndf * 2, 4, 2, 1, bias=True),
nn.LeakyReLU(0.1, inplace=True),
# state size. (ndf*2) x 8 x 8
SNConv2d(ndf * 2, ndf * 4, 3, 1, 1, bias=True),
nn.LeakyReLU(0.1, inplace=True),
SNConv2d(ndf * 4, ndf * 4, 4, 2, 1, bias=True),
nn.LeakyReLU(0.1, inplace=True),
# state size. (ndf*4) x 4 x 4
SNConv2d(ndf * 4, ndf * 8, 3, 1, 1, bias=True),
nn.LeakyReLU(0.1, inplace=True),
)
self.ln = nn.Sequential(SNLinear(ndf * 8 * 2 * 5, 1024),
nn.LeakyReLU(0.2, inplace=True),
SNLinear(1024, 1), nn.Sigmoid())
def __init__(self):
super(_netD, self).__init__()
nc = 1
ndf = 64
self.ini_w = 2
self.ini_h = 5
self.main = nn.Sequential(
# input is (nc) x 32 x 32
SNConv2d(nc, ndf, 3, 1, 1, bias=True),
nn.LeakyReLU(0.1, inplace=True),
SNConv2d(ndf, ndf, 4, 2, 1, bias=True),
nn.LeakyReLU(0.1, inplace=True),
# state size. (ndf) x 16 x 16
SNConv2d(ndf, ndf * 2, 3, 1, 1, bias=True),
nn.LeakyReLU(0.1, inplace=True),
SNConv2d(ndf * 2, ndf * 2, 4, 2, 1, bias=True),
nn.LeakyReLU(0.1, inplace=True),
# state size. (ndf*2) x 8 x 8
SNConv2d(ndf * 2, ndf * 4, 3, 1, 1, bias=True),
nn.LeakyReLU(0.1, inplace=True),
SNConv2d(ndf * 4, ndf * 4, 4, 2, 1, bias=True),
nn.LeakyReLU(0.1, inplace=True),
# state size. (ndf*4) x 4 x 4
SNConv2d(ndf * 4, ndf * 8, 3, 1, 1, bias=True),
nn.LeakyReLU(0.1, inplace=True),
)
self.snlinear = nn.Sequential(
SNLinear(ndf * 8 * self.ini_w * self.ini_h, 1), nn.Sigmoid())
def __init__(self, ndf=64, ndlayers=4):
super(SNResDiscriminator, self).__init__()
self.res_d = self.make_model(ndf, ndlayers)
self.fc = nn.Sequential(SNLinear(ndf * 16, 1), nn.Sigmoid())
def __init__(self):
super(discriminator_SN, self).__init__()
self.CNN = nn.Sequential(
SNConv1d(in_channels=2,
out_channels=16,
kernel_size=31,
stride=2,
padding=207),
# nn.BatchNorm2d(64),
nn.LeakyReLU(0.3),
SNConv1d(in_channels=16,
out_channels=32,
kernel_size=31,
stride=2,
padding=15),
# nn.BatchNorm2d(64),
nn.LeakyReLU(0.3),
SNConv1d(in_channels=32,
out_channels=32,
kernel_size=31,
stride=2,
padding=15),
# nn.BatchNorm2d(64),
nn.LeakyReLU(0.3),
SNConv1d(in_channels=32,
out_channels=64,
kernel_size=31,
stride=2,
padding=15),
# nn.BatchNorm2d(64),
nn.LeakyReLU(0.3),
SNConv1d(in_channels=64,
out_channels=64,
kernel_size=31,
stride=2,
padding=15),
# nn.BatchNorm2d(64),
nn.LeakyReLU(0.3),
SNConv1d(in_channels=64,
out_channels=128,
kernel_size=31,
stride=2,
padding=15),
# nn.BatchNorm2d(64),
nn.LeakyReLU(0.3),
SNConv1d(in_channels=128,
out_channels=128,
kernel_size=31,
stride=2,
padding=15),
# nn.BatchNorm2d(8),
nn.LeakyReLU(0.3),
SNConv1d(in_channels=128,
out_channels=256,
kernel_size=31,
stride=2,
padding=15),
nn.LeakyReLU(0.3),
SNConv1d(in_channels=256,
out_channels=256,
kernel_size=31,
stride=2,
padding=15),
nn.LeakyReLU(0.3),
SNConv1d(in_channels=256,
out_channels=512,
kernel_size=31,
stride=2,
padding=15),
nn.LeakyReLU(0.3),
SNConv1d(in_channels=512,
out_channels=1024,
kernel_size=31,
stride=2,
padding=15),
nn.LeakyReLU(0.3),
SNConv1d(in_channels=1024, out_channels=1, kernel_size=1),
nn.LeakyReLU(0.3))
self.FC1 = SNLinear(8, 1)
def __init__(self):
super(discriminator_SN_ff, self).__init__()
self.CNN = nn.Sequential(
SNConv2d(in_channels=2,
out_channels=4,
kernel_size=(1, 7),
dilation=(1, 1)),
# nn.BatchNorm2d(64),
nn.LeakyReLU(0.3),
SNConv2d(in_channels=4,
out_channels=8,
kernel_size=(7, 1),
dilation=(1, 1)),
# nn.BatchNorm2d(64),
nn.LeakyReLU(0.3),
SNConv2d(in_channels=8,
out_channels=16,
kernel_size=(5, 5),
dilation=(1, 1)),
# nn.BatchNorm2d(64),
nn.LeakyReLU(0.3),
SNConv2d(in_channels=16,
out_channels=32,
kernel_size=(5, 5),
dilation=(3, 3)),
# nn.BatchNorm2d(64),
nn.LeakyReLU(0.3),
SNConv2d(in_channels=32,
out_channels=64,
kernel_size=(5, 5),
dilation=(5, 5)),
# nn.BatchNorm2d(64),
nn.LeakyReLU(0.3),
SNConv2d(in_channels=64,
out_channels=128,
kernel_size=(5, 5),
dilation=(13, 13)),
# nn.BatchNorm2d(64),
nn.LeakyReLU(0.3),
SNConv2d(in_channels=128, out_channels=256, kernel_size=(1, 31)),
nn.LeakyReLU(0.3),
SNConv2d(in_channels=256, out_channels=512, kernel_size=(1, 31)),
nn.LeakyReLU(0.3),
SNConv2d(in_channels=512, out_channels=1024, kernel_size=(1, 31)),
nn.LeakyReLU(0.3),
SNConv2d(in_channels=1024, out_channels=1, kernel_size=1),
nn.LeakyReLU(0.3))
self.FC3 = nn.Sequential(nn.Dropout(0.5, False), SNLinear(7 * 73, 1),
nn.ReLU())
for name, param in self.FC3.named_parameters():
if 'bias' in name:
nn.init.constant_(param, 0)
elif 'weight' in name:
nn.init.xavier_normal_(param)
self.FC4 = nn.Sequential(nn.Dropout(0.5, False), nn.Linear(600, 257))
for name, param in self.FC4.named_parameters():
if 'bias' in name:
nn.init.constant_(param, 0)
elif 'weight' in name:
nn.init.xavier_normal_(param)
for name, param in self.CNN.named_parameters():
if 'bias' in name:
nn.init.constant_(param, 0.0)
elif 'weight' in name:
nn.init.xavier_normal_(param)