File size: 2,786 Bytes
e687faf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | import torch
import torch.nn as nn
class UNetBlock(nn.Module):
def __init__(self, in_channels, out_channels, down=True, use_dropout=False):
super().__init__()
if down:
self.block = nn.Sequential(
nn.Conv2d(in_channels, out_channels, 4, 2, 1, bias=False),
nn.BatchNorm2d(out_channels),
nn.LeakyReLU(0.2, inplace=True)
)
else:
self.block = nn.Sequential(
nn.ConvTranspose2d(in_channels, out_channels, 4, 2, 1, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
self.use_dropout = use_dropout
if use_dropout:
self.dropout = nn.Dropout(0.5)
def forward(self, x):
x = self.block(x)
return self.dropout(x) if self.use_dropout else x
class UNetGenerator(nn.Module):
def __init__(self, in_channels=3, out_channels=3):
super().__init__()
# Encoder
self.down1 = UNetBlock(in_channels, 64, down=True, use_dropout=False)
self.down2 = UNetBlock(64, 128)
self.down3 = UNetBlock(128, 256)
self.down4 = UNetBlock(256, 512)
self.down5 = UNetBlock(512, 512)
self.down6 = UNetBlock(512, 512)
self.down7 = UNetBlock(512, 512)
self.down8 = nn.Sequential(
nn.Conv2d(512, 512, 4, 2, 1), nn.ReLU()
)
# Decoder
self.up1 = UNetBlock(512, 512, down=False, use_dropout=True)
self.up2 = UNetBlock(1024, 512, down=False, use_dropout=True)
self.up3 = UNetBlock(1024, 512, down=False, use_dropout=True)
self.up4 = UNetBlock(1024, 512, down=False)
self.up5 = UNetBlock(1024, 256, down=False)
self.up6 = UNetBlock(512, 128, down=False)
self.up7 = UNetBlock(256, 64, down=False)
self.final = nn.Sequential(
nn.ConvTranspose2d(128, out_channels, 4, 2, 1),
nn.Tanh()
)
def forward(self, x):
# Encoder
d1 = self.down1(x)
d2 = self.down2(d1)
d3 = self.down3(d2)
d4 = self.down4(d3)
d5 = self.down5(d4)
d6 = self.down6(d5)
d7 = self.down7(d6)
bottleneck = self.down8(d7)
# Decoder with skip connections
u1 = self.up1(bottleneck)
u2 = self.up2(torch.cat([u1, d7], dim=1))
u3 = self.up3(torch.cat([u2, d6], dim=1))
u4 = self.up4(torch.cat([u3, d5], dim=1))
u5 = self.up5(torch.cat([u4, d4], dim=1))
u6 = self.up6(torch.cat([u5, d3], dim=1))
u7 = self.up7(torch.cat([u6, d2], dim=1))
out = self.final(torch.cat([u7, d1], dim=1))
return out
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