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discriminator_model.py
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discriminator_model.py
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"""
Discriminator model for CycleGAN
Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
* 2020-11-05: Initial coding
* 2022-12-21: Small revision of code, checked that it works with latest PyTorch version
"""
import torch
import torch.nn as nn
class Block(nn.Module):
def __init__(self, in_channels, out_channels, stride):
super().__init__()
self.conv = nn.Sequential(
nn.Conv2d(
in_channels,
out_channels,
4,
stride,
1,
bias=True,
padding_mode="reflect",
),
nn.InstanceNorm2d(out_channels),
nn.LeakyReLU(0.2, inplace=True),
)
def forward(self, x):
return self.conv(x)
class Discriminator(nn.Module):
def __init__(self, in_channels=3, features=[64, 128, 256, 512]):
super().__init__()
self.initial = nn.Sequential(
nn.Conv2d(
in_channels,
features[0],
kernel_size=4,
stride=2,
padding=1,
padding_mode="reflect",
),
nn.LeakyReLU(0.2, inplace=True),
)
layers = []
in_channels = features[0]
for feature in features[1:]:
layers.append(
Block(in_channels, feature, stride=1 if feature == features[-1] else 2)
)
in_channels = feature
layers.append(
nn.Conv2d(
in_channels,
1,
kernel_size=4,
stride=1,
padding=1,
padding_mode="reflect",
)
)
self.model = nn.Sequential(*layers)
def forward(self, x):
x = self.initial(x)
return torch.sigmoid(self.model(x))
def test():
x = torch.randn((5, 3, 256, 256))
model = Discriminator(in_channels=3)
preds = model(x)
print(preds.shape)
if __name__ == "__main__":
test()