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680805c | 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 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 | import torch
import torch.nn as nn
class Block(nn.Module):
def __init__(self, in_channels, out_channels, down=True, act="relu", use_dropout=False):
super(Block, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, 4, 2, 1, bias=False, padding_mode="reflect")
if down
else nn.ConvTranspose2d(in_channels, out_channels, 4, 2, 1, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU() if act == "relu" else nn.LeakyReLU(0.2),
)
self.use_dropout = use_dropout
self.dropout = nn.Dropout(0.5)
self.down = down
def forward(self, x):
x = self.conv(x)
return self.dropout(x) if self.use_dropout else x
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))
return self.final_up(torch.cat([up7, d1], 1))
def test():
x = torch.randn((1, 3, 256, 256))
model = Generator(in_channels=3, features=64)
preds = model(x)
print(preds.shape)
if __name__ == "__main__":
test() |