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import torch
import torch.nn as nn
from models.base import Block
class Generator(nn.Module):
def __init__(self, in_channels=3, features=64):
super().__init__()
self.initial_down = nn.Sequential(
nn.Conv2d(in_channels, features, 4, 2, 1, padding_mode="reflect"),
nn.LeakyReLU(0.2),
)
self.down1 = Block(features, features * 2, down=True, act="leaky", use_dropout=False)
self.down2 = Block(features * 2, features * 4, down=True, act="leaky", use_dropout=False)
self.down3 = Block(features * 4, features * 8, down=True, act="leaky", use_dropout=False)
self.down4 = Block(features * 8, features * 8, down=True, act="leaky", use_dropout=False)
self.down5 = Block(features * 8, features * 8, down=True, act="leaky", use_dropout=False)
self.down6 = Block(features * 8, features * 8, down=True, act="leaky", use_dropout=False)
self.bottleneck = nn.Sequential(
nn.Conv2d(features * 8, features * 8, 4, 2, 1),
nn.ReLU()
)
self.up1 = Block(features * 8, features * 8, down=False, act="relu", use_dropout=True)
self.up2 = Block(features * 8 * 2, features * 8, down=False, act="relu", use_dropout=True)
self.up3 = Block(features * 8 * 2, features * 8, down=False, act="relu", use_dropout=True)
self.up4 = Block(features * 8 * 2, features * 8, down=False, act="relu", use_dropout=False)
self.up5 = Block(features * 8 * 2, features * 4, down=False, act="relu", use_dropout=False)
self.up6 = Block(features * 4 * 2, features * 2, down=False, act="relu", use_dropout=False)
self.up7 = Block(features * 2 * 2, features, down=False, act="relu", use_dropout=False)
self.final_up = nn.Sequential(
nn.ConvTranspose2d(features * 2, in_channels, kernel_size=4, stride=2, padding=1),
nn.Tanh(),
)
def forward(self, x):
d1 = self.initial_down(x)
d2 = self.down1(d1)
d3 = self.down2(d2)
d4 = self.down3(d3)
d5 = self.down4(d4)
d6 = self.down5(d5)
d7 = self.down6(d6)
bottleneck = self.bottleneck(d7)
up1 = self.up1(bottleneck)
up2 = self.up2(torch.cat([up1, d7], 1))
up3 = self.up3(torch.cat([up2, d6], 1))
up4 = self.up4(torch.cat([up3, d5], 1))
up5 = self.up5(torch.cat([up4, d4], 1))
up6 = self.up6(torch.cat([up5, d3], 1))
up7 = self.up7(torch.cat([up6, d2], 1))
final_up = self.final_up(torch.cat([up7, d1], 1))
return final_up
def test():
# Test Case for Generator Model
x = torch.randn((1, 3, 256, 256))
gen = Generator()
print(f"Generator Output Shape: {gen(x).shape}") |