def __init__(self, latent_size, scale=0.01):
super().__init__(
ConvEq(latent_size, latent_size, 1, scale=scale),
ReLU(),
ConvEq(latent_size, latent_size, 1, scale=scale),
ReLU(),
ConvEq(latent_size, latent_size, 1, scale=scale),
ReLU(),
)
def __init__(self, in_channels, out_channels, latent_size):
super().__init__()
self.conv = ConvTransposeEq(in_channels, out_channels, 4)
self.noise = AdditiveNoise(out_channels)
self.relu = ReLU()
self.norm = AdaptiveInstanceNorm(out_channels, latent_size)
def __init__(self, in_channels, out_channels, latent_size, upsample=False):
super().__init__()
self.upsample = Upsample(scale_factor=2) if upsample else NoOp()
self.conv = ConvEq(in_channels, out_channels, 3, padding=1)
self.noise = AdditiveNoise(out_channels)
self.relu = ReLU()
self.norm = AdaptiveInstanceNorm(out_channels, latent_size)
def __init__(self, in_channels, out_channels, latent_size):
super().__init__()
self.input = nn.Parameter(torch.Tensor(1, out_channels, 4, 4))
self.noise = AdditiveNoise(out_channels)
self.relu = ReLU()
self.norm = AdaptiveInstanceNorm(out_channels, latent_size)
torch.nn.init.constant_(self.input, 1.0)
def build_level_layers(level, base_channels=16):
in_channels = base_channels * 2 ** (max_pow - level - 1)
out_channels = base_channels * 2 ** (max_pow - level)
if level == 0:
conv = nn.Sequential(MidBlock(in_channels, in_channels), ZeroBlock(in_channels))
else:
conv = nn.Sequential(
MidBlock(in_channels, in_channels),
MidBlock(in_channels, out_channels, downsample=True),
)
from_rgb = nn.Sequential(ConvEq(3, in_channels, 1), ReLU())
return nn.ModuleDict(
OrderedDict(
{
"conv": conv,
"from_rgb": from_rgb,
}
)
)
def __init__(self, in_channels, out_channels, downsample=False):
super().__init__(
ConvEq(in_channels, out_channels, 3, padding=1),
ReLU(),
Upsample(scale_factor=0.5) if downsample else NoOp(),
)
def __init__(self, in_channels):
super().__init__(ConvEq(in_channels, in_channels, 4), ReLU(),
ConvEq(in_channels, 1, 1))