def __init__(self, nc, ndf, alpha, beta, ngpu=1):
super(DiscriminatorNetVBN, self).__init__()
self.ngpu = ngpu
self.net = nnrd.RelevanceNet(
nnrd.Layer(
nnrd.FirstConvolution(nc, ndf, 5, 1, 2),
nnrd.ReLu(),
),
nnrd.Layer(
nnrd.NextConvolution(ndf, ndf, 4, '0', 2, 1, alpha=alpha),
nnrd.BatchNorm2d(ndf),
nnrd.ReLu(),
),
# state size. (ndf) x 32 x 32
nnrd.Layer(
nnrd.NextConvolution(ndf, ndf * 2, 4, '1', 2, 1, alpha=alpha),
nnrd.BatchNorm2d(ndf * 2),
nnrd.ReLu(),
),
# state size. (ndf*2) x 16 x 16
nnrd.Layer(
nnrd.NextConvolution(ndf * 2,
ndf * 4,
4,
'2',
2,
1,
alpha=alpha),
nnrd.BatchNorm2d(ndf * 4),
nnrd.ReLu(),
),
# state size. (ndf*4) x 8 x 8
nnrd.Layer(
nnrd.NextConvolution(ndf * 4,
ndf * 8,
4,
'3',
2,
1,
alpha=alpha),
nnrd.BatchNorm2d(ndf * 8),
nnrd.ReLu(),
),
# state size. (ndf*8) x 4 x 4
nnrd.Layer(nnrd.NextConvolution(ndf * 8, 1, 4, '4', 1, 0),
nn.Sigmoid()))
def __init__(self, nc, ndf, alpha, ngpu=1):
super(DiscriminatorNetLessCheckerboardToCanonical, self).__init__()
self.relevance = None
self.ngpu = ngpu
self.net = nnrd.RelevanceNetAlternate(
nnrd.Layer(
OrderedDict([
('conv1',
nnrd.FirstConvolution(in_channels=nc,
out_channels=ndf,
kernel_size=3,
stride=1,
padding=0)),
('relu1', nnrd.ReLu()),
])),
nnrd.Layer(
OrderedDict([
('conv2',
nnrd.NextConvolution(in_channels=ndf,
out_channels=ndf,
kernel_size=4,
name='0',
stride=2,
padding=1,
alpha=alpha)),
('bn2', nnrd.BatchNorm2d(ndf)),
('relu2', nnrd.ReLu()),
('dropou2', nnrd.Dropout(0.3)),
])),
# state size. (ndf) x 32 x 32
nnrd.Layer(
OrderedDict([
('conv3',
nnrd.NextConvolution(in_channels=ndf,
out_channels=ndf * 2,
kernel_size=4,
name='1',
stride=2,
padding=1,
alpha=alpha)),
('bn3', nnrd.BatchNorm2d(ndf * 2)),
('relu3', nnrd.ReLu()),
('dropout3', nnrd.Dropout(0.3)),
])),
# state size. (ndf*2) x 16 x 16
nnrd.Layer(
OrderedDict([
('conv4',
nnrd.NextConvolution(in_channels=ndf * 2,
out_channels=ndf * 4,
kernel_size=4,
name='2',
stride=2,
padding=1,
alpha=alpha)),
('bn4', nnrd.BatchNorm2d(ndf * 4)),
('relu4', nnrd.ReLu()),
('dropout4', nnrd.Dropout(0.3)),
])), # state size. (ndf*2) x 16 x 16
# state size. (ndf*4) x 8 x 8
nnrd.Layer(
OrderedDict([
('conv5',
nnrd.NextConvolutionEps(in_channels=ndf * 4,
out_channels=ndf * 8,
kernel_size=4,
name='3',
stride=2,
padding=1,
epsilon=0.01)),
('bn5', nnrd.BatchNorm2d(ndf * 8)),
('relu5', nnrd.ReLu()),
('dropout5', nnrd.Dropout(0.3)),
])),
)
self.lastConvolution = nnrd.LastConvolutionEps(in_channels=ndf * 8,
out_channels=1,
kernel_size=4,
name='4',
stride=1,
padding=0,
epsilon=0.01)
self.sigmoid = nn.Sigmoid()
self.lastReLU = nnrd.ReLu()
def __init__(self, nc, ndf, alpha, beta, ngpu=1):
super(DiscriminatorNetLessCheckerboardAlternate, self).__init__()
self.relevance = None
self.ngpu = ngpu
self.net = nnrd.RelevanceNetAlternate(
nnrd.Layer(
nnrd.FirstConvolution(in_channels=nc,
out_channels=ndf,
kernel_size=5,
stride=1,
padding=0),
nnrd.ReLu(),
),
nnrd.Layer(
nnrd.NextConvolution(in_channels=ndf,
out_channels=ndf,
kernel_size=4,
name='0',
stride=2,
padding=1,
alpha=alpha),
nnrd.BatchNorm2d(ndf),
nnrd.ReLu(),
nnrd.Dropout(0.3),
),
# state size. (ndf) x 32 x 32
nnrd.Layer(
nnrd.NextConvolution(in_channels=ndf,
out_channels=ndf * 2,
kernel_size=4,
name='1',
stride=2,
padding=1,
alpha=alpha),
nnrd.BatchNorm2d(ndf * 2),
nnrd.ReLu(),
nnrd.Dropout(0.3),
),
# state size. (ndf*2) x 16 x 16
nnrd.Layer(
nnrd.NextConvolution(in_channels=ndf * 2,
out_channels=ndf * 4,
kernel_size=4,
name='2',
stride=2,
padding=1,
alpha=alpha),
nnrd.BatchNorm2d(ndf * 4),
nnrd.ReLu(),
nnrd.Dropout(0.3),
), # state size. (ndf*2) x 16 x 16
# state size. (ndf*4) x 8 x 8
nnrd.Layer(
nnrd.NextConvolutionEps(in_channels=ndf * 4,
out_channels=ndf * 8,
kernel_size=4,
name='3',
stride=2,
padding=1),
nnrd.BatchNorm2d(ndf * 8),
nnrd.ReLu(),
nnrd.Dropout(0.3),
),
# state size. (ndf*8) x 4 x 4
nnrd.Layer(
nnrd.LastConvolutionEps(in_channels=ndf * 8,
out_channels=1,
kernel_size=4,
name='4',
stride=1,
padding=0), ))
self.sigmoid = nn.Sigmoid()
self.lastReLU = nnrd.ReLu()
def __init__(self, nc, ndf, alpha, ngpu=1):
super(SmoothingLayerDiscriminator, self).__init__()
self.relevance = None
self.ngpu = ngpu
self.net = nnrd.RelevanceNetAlternate(
nnrd.Layer(
OrderedDict([
('conv1',
nnrd.FirstConvolution(in_channels=nc,
out_channels=nc,
kernel_size=3,
stride=1,
padding=0)),
('relu1', nnrd.ReLu()),
])),
nnrd.Layer(
OrderedDict([
('conv2',
nnrd.NextConvolution(in_channels=nc,
out_channels=ndf,
kernel_size=4,
name='0',
stride=2,
padding=1,
alpha=alpha)),
('bn2', nnrd.BatchNorm2d(ndf)),
('relu2', nnrd.ReLu()),
('dropou2', nnrd.Dropout(0.3)),
])),
# state size. (ndf) x 32 x 32
nnrd.Layer(
OrderedDict([
('conv3',
nnrd.NextConvolution(in_channels=ndf,
out_channels=ndf * 2,
kernel_size=4,
name='1',
stride=2,
padding=1,
alpha=alpha)),
('bn3', nnrd.BatchNorm2d(ndf * 2)),
('relu3', nnrd.ReLu()),
('dropout3', nnrd.Dropout(0.3)),
])),
# state size. (ndf*2) x 16 x 16
nnrd.Layer(
OrderedDict([
('conv4',
nnrd.NextConvolution(in_channels=ndf * 2,
out_channels=ndf * 4,
kernel_size=4,
name='2',
stride=2,
padding=1,
alpha=alpha)),
('bn4', nnrd.BatchNorm2d(ndf * 4)),
('relu4', nnrd.ReLu()),
('dropout4', nnrd.Dropout(0.3)),
])), # state size. (ndf*2) x 16 x 16
# state size. (ndf*4) x 8 x 8
nnrd.Layer(
OrderedDict([
('conv5',
nnrd.NextConvolutionEps(in_channels=ndf * 4,
out_channels=ndf * 8,
kernel_size=4,
name='3',
stride=2,
padding=1,
epsilon=0.01)),
('bn5', nnrd.BatchNorm2d(ndf * 8)),
('relu5', nnrd.ReLu()),
('dropout5', nnrd.Dropout(0.3)),
])),
)
self.lastConvolution = nnrd.LastConvolutionEps(in_channels=ndf * 8,
out_channels=1,
kernel_size=4,
name='4',
stride=1,
padding=0,
epsilon=0.01)
self.sigmoid = nn.Sigmoid()
self.lastReLU = nnrd.ReLu()
# Do not update weights in smoothing layer
for parameter in self.net[0][0].parameters():
parameter.requires_grad = False