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models.py
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models.py
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import torch.nn as nn
import torch.nn.functional as F
import torch
def weights_init_normal(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find('BatchNorm2d') != -1:
torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
torch.nn.init.constant_(m.bias.data, 0.0)
##############################
# U-NET
##############################
class UNetDown(nn.Module):
def __init__(self, in_size, out_size, normalize=True, dropout=0.0):
super(UNetDown, self).__init__()
layers = [nn.Conv2d(in_size, out_size, 4, 2, 1, bias=False)]
if normalize:
layers.append(nn.InstanceNorm2d(out_size))
layers.append(nn.LeakyReLU(0.2))
if dropout:
layers.append(nn.Dropout(dropout))
self.model = nn.Sequential(*layers)
def forward(self, x):
return self.model(x)
class UNetUp(nn.Module):
def __init__(self, in_size, out_size, dropout=0.0):
super(UNetUp, self).__init__()
layers = [nn.ConvTranspose2d(in_size, out_size, 4, 2, 1, bias=False),
nn.InstanceNorm2d(out_size),
nn.ReLU(inplace=True)]
if dropout:
layers.append(nn.Dropout(dropout))
self.model = nn.Sequential(*layers)
def forward(self, x, skip_input):
x = self.model(x)
x = torch.cat((x, skip_input), 1)
return x
class GeneratorUNet(nn.Module):
def __init__(self, in_channels=3, out_channels=3):
super(GeneratorUNet, self).__init__()
self.down1 = UNetDown(in_channels, 64, normalize=False)
self.down2 = UNetDown(64, 128)
self.down3 = UNetDown(128, 256)
self.down4 = UNetDown(256, 512)
self.down5 = UNetDown(512, 512)
self.down6 = UNetDown(512, 512)
self.down7 = UNetDown(512, 512)
self.down8 = UNetDown(512, 512, normalize=False)
self.up1 = UNetUp(512, 512, dropout=0.5)
self.up2 = UNetUp(1024, 512, dropout=0.5)
self.up3 = UNetUp(1024, 512, dropout=0.5)
self.up4 = UNetUp(1024, 512, dropout=0.5)
self.up5 = UNetUp(1024, 256)
self.up6 = UNetUp(512, 128)
self.up7 = UNetUp(256, 64)
self.final = nn.Sequential(
nn.Upsample(scale_factor=2),
nn.ZeroPad2d((1, 0, 1, 0)),
nn.Conv2d(128, out_channels, 4, padding=1),
nn.Tanh()
)
def forward(self, x):
# U-Net generator with skip connections from encoder to decoder
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)
d8 = self.down8(d7)
u1 = self.up1(d8, d7)
u2 = self.up2(u1, d6)
u3 = self.up3(u2, d5)
u4 = self.up4(u3, d4)
u5 = self.up5(u4, d3)
u6 = self.up6(u5, d2)
u7 = self.up7(u6, d1)
return self.final(u7)
##############################
# Discriminator #
##############################
class Discriminator(nn.Module):
def __init__(self, in_channels=3):
super(Discriminator, self).__init__()
def discriminator_block(in_filters, out_filters, normalization=True):
"""Returns downsampling layers of each discriminator block"""
layers = [nn.Conv2d(in_filters, out_filters, 4, stride=2, padding=1)]
if normalization:
layers.append(nn.InstanceNorm2d(out_filters))
layers.append(nn.LeakyReLU(0.2, inplace=True))
return layers
self.model = nn.Sequential(
*discriminator_block(in_channels * 2, 64, normalization=False),
*discriminator_block(64, 128),
*discriminator_block(128, 256),
*discriminator_block(256, 512),
nn.ZeroPad2d((1, 0, 1, 0)),
nn.Conv2d(512, 1, 4, padding=1, bias=False)
)
def forward(self, img_A, img_B):
# Concatenate image and condition image by channels to produce input
img_input = torch.cat((img_A, img_B), 1)
return self.model(img_input)
class AutoEncoder(nn.Module):
def __init__(self):
super(AutoEncoder, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=(5,5), stride=(1,1))
self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=(5,5), stride=(1,1))
self.conv3 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=(5,5), stride=(1,1))
self.fc1 = nn.Linear(in_features=28*28*64, out_features=2048)
self.dfc1 = nn.Linear(in_features=2048, out_features=29*29*64)
self.dconv3 = nn.ConvTranspose2d(in_channels=64, out_channels=32, kernel_size=(5,5), stride=(2,2))
self.dconv2 = nn.ConvTranspose2d(in_channels=32, out_channels=16, kernel_size=(6,6), stride=(2,2))
self.dconv1 = nn.ConvTranspose2d(in_channels=16, out_channels=3, kernel_size=(6,6), stride=(2,2))
def forward(self,x):
######################
#ENCODER
######################
x = self.conv1(x)
x = F.relu(x)
x = F.max_pool2d(x, 2, 2)
x = self.conv2(x)
x = F.relu(x)
x = F.max_pool2d(x, 2, 2)
x = self.conv3(x)
x = F.relu(x)
x = F.max_pool2d(x, 2, 2)
# feed output to fully connected layer #
## vectorizing the input matrix
x = x.view(-1, 28*28*64)
x = F.relu(self.fc1(x))
######################
#DECODER
######################
x = F.relu(self.dfc1(x))
x = x.view(-1, 64,29,29)
x = self.dconv3(x)
x = F.relu(x)
x = self.dconv2(x)
x = F.relu(x)
x = self.dconv1(x)
x = F.relu(x)
return x
def encode(self,x):
x = self.conv1(x)
x = F.relu(x)
x = F.max_pool2d(x, 2, 2)
x = self.conv2(x)
x = F.relu(x)
x = F.max_pool2d(x, 2, 2)
x = self.conv3(x)
x = F.relu(x)
x = F.max_pool2d(x, 2, 2)
# feed output to fully connected layer #
## vectorizing the input matrix
x = x.view(-1, 28*28*64)
x = F.relu(self.fc1(x))
return x
def decode(self,x):
x = F.relu(self.dfc1(x))
x = x.view(-1, 64,29,29)
x = self.dconv3(x)
x = F.relu(x)
x = self.dconv2(x)
x = F.relu(x)
x = self.dconv1(x)
x = F.relu(x)
return x
class AutoEncoder64(nn.Module):
def __init__(self):
super(AutoEncoder64, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=(5,5), stride=(1,1))
self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=(5,5), stride=(1,1))
self.conv3 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=(5,5), stride=(1,1))
self.fc1 = nn.Linear(in_features=28*28*64, out_features=64)
self.dfc1 = nn.Linear(in_features=64, out_features=29*29*64)
self.dconv3 = nn.ConvTranspose2d(in_channels=64, out_channels=32, kernel_size=(5,5), stride=(2,2))
self.dconv2 = nn.ConvTranspose2d(in_channels=32, out_channels=16, kernel_size=(6,6), stride=(2,2))
self.dconv1 = nn.ConvTranspose2d(in_channels=16, out_channels=3, kernel_size=(6,6), stride=(2,2))
def forward(self,x):
######################
#ENCODER
######################
x = self.conv1(x)
x = F.relu(x)
x = F.max_pool2d(x, 2, 2)
x = self.conv2(x)
x = F.relu(x)
x = F.max_pool2d(x, 2, 2)
x = self.conv3(x)
x = F.relu(x)
x = F.max_pool2d(x, 2, 2)
# feed output to fully connected layer #
## vectorizing the input matrix
x = x.view(-1, 28*28*64)
x = F.relu(self.fc1(x))
######################
#DECODER
######################
x = F.relu(self.dfc1(x))
x = x.view(-1, 64,29,29)
x = self.dconv3(x)
x = F.relu(x)
x = self.dconv2(x)
x = F.relu(x)
x = self.dconv1(x)
x = F.relu(x)
return x
def encode(self,x):
x = self.conv1(x)
x = F.relu(x)
x = F.max_pool2d(x, 2, 2)
x = self.conv2(x)
x = F.relu(x)
x = F.max_pool2d(x, 2, 2)
x = self.conv3(x)
x = F.relu(x)
x = F.max_pool2d(x, 2, 2)
# feed output to fully connected layer #
## vectorizing the input matrix
x = x.view(-1, 28*28*64)
x = F.relu(self.fc1(x))
return x
def decode(self,x):
x = F.relu(self.dfc1(x))
x = x.view(-1, 64,29,29)
x = self.dconv3(x)
x = F.relu(x)
x = self.dconv2(x)
x = F.relu(x)
x = self.dconv1(x)
x = F.relu(x)
return x