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from torch import nn
from layers.normalization import AdaIN
class DestyleResBlock(nn.Module):
def __init__(self, channels_out, kernel_size, channels_in=None, stride=1, dilation=1, padding=1, use_dropout=False):
super(DestyleResBlock, self).__init__()
# uses 1x1 convolutions for downsampling
if not channels_in or channels_in == channels_out:
channels_in = channels_out
self.projection = None
else:
self.projection = nn.Conv2d(channels_in, channels_out, kernel_size=1, stride=stride, dilation=1)
self.use_dropout = use_dropout
self.conv1 = nn.Conv2d(channels_in, channels_out, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation)
self.lrelu1 = nn.LeakyReLU(negative_slope=0.2, inplace=True)
self.conv2 = nn.Conv2d(channels_out, channels_out, kernel_size=kernel_size, stride=1, padding=padding, dilation=dilation)
self.adain = AdaIN()
if self.use_dropout:
self.dropout = nn.Dropout()
self.lrelu2 = nn.LeakyReLU(negative_slope=0.2, inplace=True)
def forward(self, x, feat):
residual = x
out = self.conv1(x)
out = self.lrelu1(out)
out = self.conv2(out)
_, _, h, w = out.size()
out = self.adain(out, feat)
if self.use_dropout:
out = self.dropout(out)
if self.projection:
residual = self.projection(x)
out = out + residual
out = self.lrelu2(out)
return out
class ResBlock(nn.Module):
def __init__(self, channels_out, kernel_size, channels_in=None, stride=1, dilation=1, padding=1, use_dropout=False):
super(ResBlock, self).__init__()
# uses 1x1 convolutions for downsampling
if not channels_in or channels_in == channels_out:
channels_in = channels_out
self.projection = None
else:
self.projection = nn.Conv2d(channels_in, channels_out, kernel_size=1, stride=stride, dilation=1)
self.use_dropout = use_dropout
self.conv1 = nn.Conv2d(channels_in, channels_out, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation)
self.lrelu1 = nn.LeakyReLU(negative_slope=0.2, inplace=True)
self.conv2 = nn.Conv2d(channels_out, channels_out, kernel_size=kernel_size, stride=1, padding=padding, dilation=dilation)
self.n2 = nn.BatchNorm2d(channels_out)
if self.use_dropout:
self.dropout = nn.Dropout()
self.lrelu2 = nn.LeakyReLU(negative_slope=0.2, inplace=True)
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.lrelu1(out)
out = self.conv2(out)
# out = self.n2(out)
if self.use_dropout:
out = self.dropout(out)
if self.projection:
residual = self.projection(x)
out = out + residual
out = self.lrelu2(out)
return out
class Destyler(nn.Module):
def __init__(self, in_features, num_features):
super(Destyler, self).__init__()
self.fc1 = nn.Linear(in_features, num_features)
self.fc2 = nn.Linear(num_features, num_features)
self.fc3 = nn.Linear(num_features, num_features)
self.fc4 = nn.Linear(num_features, num_features)
self.fc5 = nn.Linear(num_features, num_features)
def forward(self, x):
x = self.fc1(x)
x = self.fc2(x)
x = self.fc3(x)
x = self.fc4(x)
x = self.fc5(x)
return x
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