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Models.py
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import torch
import torch.nn as nn
####################################################################################################################
################################################## DISCRIMINATOR ###################################################
####################################################################################################################
class CNN_Block(nn.Module):
def __init__(self,in_channels,out_channels,stride=2):
super().__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, 4, stride, bias=False, padding_mode="reflect"),
nn.BatchNorm2d(out_channels),
nn.LeakyReLU(0.2)
)
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*2, features[0], kernel_size=4, stride=2, padding=1, padding_mode="reflect"),
nn.LeakyReLU(0.2)
)
layers = []
in_channels = features[0]
for feature in features[1:]:
layers.append(
CNN_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,y):
### X = Correct Satellite Image
### Y = Correct/Fake Image
x = torch.cat([x,y],dim=1)
x = self.initial(x)
return self.model(x)
####################################################################################################################
#################################################### GENERATOR #####################################################
####################################################################################################################
class Block(nn.Module):
def __init__(self, in_channels, out_channels, down = True, act="relu", use_dropout=False):
super().__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)
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)
) # 128 X 128
##############################################################################
################################## ENCODER ###################################
##############################################################################
self.down1 = Block(features, features*2, down=True, act="leaky", use_dropout=False) # 64 X 64
self.down2 = Block(features*2, features*4, down=True, act="leaky", use_dropout=False) # 32 X 32
self.down3 = Block(features*4, features*8, down=True, act="leaky", use_dropout=False) # 16 X 16
self.down4 = Block(features*8, features*8, down=True, act="leaky", use_dropout=False) # 8 X 8
self.down5 = Block(features*8, features*8, down=True, act="leaky", use_dropout=False) # 4 X 4
self.down6 = Block(features*8, features*8, down=True, act="leaky", use_dropout=False) # 2 X 2
##############################################################################
################################# BOTTLENECK #################################
##############################################################################
self.bottleneck = nn.Sequential(
nn.Conv2d(features*8,features*8,4,2,1,padding_mode="reflect"), # 1 X 1
nn.ReLU()
)
##############################################################################
################################## DECODER ###################################
##############################################################################
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_disc():
# x = torch.randn((1,3,256,256))
# y = torch.randn((1,3,256,256))
# Model = Discriminator()
# pred = Model(x,y)
# print(pred.shape)
# def test_gen():
# x = torch.randn((1, 3, 256, 256))
# model = Generator(in_channels=3, features=64)
# preds = model(x)
# print(preds.shape)
# test_gen()