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from torch import nn
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels, use_act, **kwargs):
super().__init__()
self.cnn = nn.Conv2d(
in_channels,
out_channels,
**kwargs,
bias=True,
)
self.act = nn.LeakyReLU(0.2, inplace=True) if use_act else nn.Identity()
def forward(self, x):
return self.act(self.cnn(x))
class UpsampleBlock(nn.Module):
def __init__(self, in_c, scale_factor=2):
super().__init__()
self.upsample = nn.Upsample(scale_factor=scale_factor, mode="nearest")
self.conv = nn.Conv2d(in_c, in_c, 3, 1, 1, bias=True)
self.act = nn.LeakyReLU(0.2, inplace=True)
def forward(self, x):
return self.act(self.conv(self.upsample(x)))
class DenseResidualBlock(nn.Module):
def __init__(self, in_channels, channels=32, residual_beta=0.2):
super().__init__()
self.residual_beta = residual_beta
self.blocks = nn.ModuleList()
for i in range(5):
self.blocks.append(
ConvBlock(
in_channels + channels * i,
channels if i <= 3 else in_channels,
kernel_size=3,
stride=1,
padding=1,
use_act=True if i <= 3 else False,
)
)
def forward(self, x):
new_inputs = x
for block in self.blocks:
out = block(new_inputs)
new_inputs = torch.cat([new_inputs, out], dim=1)
return self.residual_beta * out + x
class RRDB(nn.Module):
def __init__(self, in_channels, residual_beta=0.2):
super().__init__()
self.residual_beta = residual_beta
self.rrdb = nn.Sequential(*[DenseResidualBlock(in_channels) for _ in range(3)])
def forward(self, x):
return self.rrdb(x) * self.residual_beta + x
class Generator(nn.Module):
def __init__(self, in_channels=3, num_channels=64, num_blocks=23):
super().__init__()
self.initial = nn.Conv2d(
in_channels,
num_channels,
kernel_size=3,
stride=1,
padding=1,
bias=True,
)
self.residuals = nn.Sequential(*[RRDB(num_channels) for _ in range(num_blocks)])
self.conv = nn.Conv2d(num_channels, num_channels, kernel_size=3, stride=1, padding=1)
self.upsamples = nn.Sequential(
UpsampleBlock(num_channels), UpsampleBlock(num_channels),
)
self.final = nn.Sequential(
nn.Conv2d(num_channels, num_channels, 3, 1, 1, bias=True),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(num_channels, in_channels, 3, 1, 1, bias=True),
)
def forward(self, x):
initial = self.initial(x)
x = self.conv(self.residuals(initial)) + initial
x = self.upsamples(x)
return self.final(x)
class Discriminator(nn.Module):
def __init__(self, in_channels=3, features=[64, 64, 128, 128, 256, 256, 512, 512]):
super().__init__()
blocks = []
for idx, feature in enumerate(features):
blocks.append(
ConvBlock(
in_channels,
feature,
kernel_size=3,
stride=1 + idx % 2,
padding=1,
use_act=True,
),
)
in_channels = feature
self.blocks = nn.Sequential(*blocks)
self.classifier = nn.Sequential(
nn.AdaptiveAvgPool2d((6, 6)),
nn.Flatten(),
nn.Linear(512 * 6 * 6, 1024),
nn.LeakyReLU(0.2, inplace=True),
nn.Linear(1024, 1),
)
def forward(self, x):
x = self.blocks(x)
return self.classifier(x)
def initialize_weights(model, scale=0.1):
for m in model.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight.data)
m.weight.data *= scale
elif isinstance(m, nn.Linear):
nn.init.kaiming_normal_(m.weight.data)
m.weight.data *= scale
def test():
gen = Generator()
disc = Discriminator()
low_res = 24
x = torch.randn((5, 3, low_res, low_res))
gen_out = gen(x)
disc_out = disc(gen_out)
print(gen_out.shape)
print(disc_out.shape)
test()
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