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model.py
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import torch.nn as nn
import torch.nn.functional as F
from utils import weights_init_normal
import torch
class ResidualBlock(nn.Module):
def __init__(self, in_features):
super(ResidualBlock, self).__init__()
conv_block = [ nn.ReflectionPad2d(1),
nn.Conv2d(in_features, in_features, 3),
nn.InstanceNorm2d(in_features),
nn.ReLU(inplace=True),
nn.ReflectionPad2d(1),
nn.Conv2d(in_features, in_features, 3),
nn.InstanceNorm2d(in_features) ]
self.conv_block = nn.Sequential(*conv_block)
def forward(self, x):
return x + self.conv_block(x)
class Generator_S2F(nn.Module):
def __init__(self,init_weights=False):
super(Generator_S2F, self).__init__()
# Initial convolution block
self.conv1_b=nn.Sequential(nn.ReflectionPad2d(3),
nn.Conv2d(3, 64, 7),
nn.InstanceNorm2d(64),
nn.ReLU(inplace=True))
self.downconv2_b=nn.Sequential(nn.Conv2d(64, 128, 3, stride=2, padding=1),
nn.InstanceNorm2d(128),
nn.ReLU(inplace=True))
self.downconv3_b=nn.Sequential(nn.Conv2d(128, 256, 3, stride=2, padding=1),
nn.InstanceNorm2d(256),
nn.ReLU(inplace=True))
self.conv4_b=nn.Sequential(ResidualBlock(256))
self.conv5_b=nn.Sequential(ResidualBlock(256))
self.conv6_b=nn.Sequential(ResidualBlock(256))
self.conv7_b=nn.Sequential(ResidualBlock(256))
self.conv8_b=nn.Sequential(ResidualBlock(256))
self.conv9_b=nn.Sequential(ResidualBlock(256))
self.conv10_b=nn.Sequential(ResidualBlock(256))
self.conv11_b=nn.Sequential(ResidualBlock(256))
self.conv12_b=nn.Sequential(ResidualBlock(256))
self.upconv13_b=nn.Sequential(nn.ConvTranspose2d(256,128,3,stride=2,padding=1,output_padding=1),
nn.InstanceNorm2d(128),
nn.ReLU(inplace=True))
self.upconv14_b=nn.Sequential(nn.ConvTranspose2d(128,64,3,stride=2,padding=1,output_padding=1),
nn.InstanceNorm2d(64),
nn.ReLU(inplace=True))
self.conv15_b=nn.Sequential(nn.ReflectionPad2d(3),nn.Conv2d(64, 3, 7))
if init_weights:
self.apply(weights_init_normal)
@staticmethod
def from_file(file_path: str) -> nn.Module:
model = Generator_S2F(init_weights=True)
return model
def forward(self,xin):
x=self.conv1_b(xin)
x=self.downconv2_b(x)
x=self.downconv3_b(x)
x=self.conv4_b(x)
x=self.conv5_b(x)
x=self.conv6_b(x)
x=self.conv7_b(x)
x=self.conv8_b(x)
x=self.conv9_b(x)
x=self.conv10_b(x)
x=self.conv11_b(x)
x=self.conv12_b(x)
x=self.upconv13_b(x)
x=self.upconv14_b(x)
x=self.conv15_b(x)
xout=x+xin
return xout.tanh()
class Generator_F2S(nn.Module):
def __init__(self,init_weights=False):
super(Generator_F2S, self).__init__()
# Initial convolution block
self.conv1_b=nn.Sequential(nn.ReflectionPad2d(3),
nn.Conv2d(4, 64, 7),
nn.InstanceNorm2d(64),
nn.ReLU(inplace=True))
self.downconv2_b=nn.Sequential(nn.Conv2d(64, 128, 3, stride=2, padding=1),
nn.InstanceNorm2d(128),
nn.ReLU(inplace=True))
self.downconv3_b=nn.Sequential(nn.Conv2d(128, 256, 3, stride=2, padding=1),
nn.InstanceNorm2d(256),
nn.ReLU(inplace=True))
self.conv4_b=nn.Sequential(ResidualBlock(256))
self.conv5_b=nn.Sequential(ResidualBlock(256))
self.conv6_b=nn.Sequential(ResidualBlock(256))
self.conv7_b=nn.Sequential(ResidualBlock(256))
self.conv8_b=nn.Sequential(ResidualBlock(256))
self.conv9_b=nn.Sequential(ResidualBlock(256))
self.conv10_b=nn.Sequential(ResidualBlock(256))
self.conv11_b=nn.Sequential(ResidualBlock(256))
self.conv12_b=nn.Sequential(ResidualBlock(256))
self.upconv13_b=nn.Sequential(nn.ConvTranspose2d(256,128,3,stride=2,padding=1,output_padding=1),
nn.InstanceNorm2d(128),
nn.ReLU(inplace=True))
self.upconv14_b=nn.Sequential(nn.ConvTranspose2d(128,64,3,stride=2,padding=1,output_padding=1),
nn.InstanceNorm2d(64),
nn.ReLU(inplace=True))
self.conv15_b=nn.Sequential(nn.ReflectionPad2d(3),nn.Conv2d(64, 3, 7))
if init_weights:
self.apply(weights_init_normal)
@staticmethod
def from_file(file_path: str) -> nn.Module:
model = Generator_F2S(init_weights=True)
return model
def forward(self,xin,mask):
x=torch.cat((xin,mask),1)
x=self.conv1_b(x)
x=self.downconv2_b(x)
x=self.downconv3_b(x)
x=self.conv4_b(x)
x=self.conv5_b(x)
x=self.conv6_b(x)
x=self.conv7_b(x)
x=self.conv8_b(x)
x=self.conv9_b(x)
x=self.conv10_b(x)
x=self.conv11_b(x)
x=self.conv12_b(x)
x=self.upconv13_b(x)
x=self.upconv14_b(x)
x=self.conv15_b(x)
xout=x+xin
return xout.tanh()
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
# A bunch of convolutions one after another
model = [ nn.Conv2d(3, 64, 4, stride=2, padding=1),
nn.LeakyReLU(0.2, inplace=True) ]
model += [ nn.Conv2d(64, 128, 4, stride=2, padding=1),
nn.InstanceNorm2d(128),
nn.LeakyReLU(0.2, inplace=True) ]
model += [ nn.Conv2d(128, 256, 4, stride=2, padding=1),
nn.InstanceNorm2d(256),
nn.LeakyReLU(0.2, inplace=True) ]
model += [ nn.Conv2d(256, 512, 4, padding=1),
nn.InstanceNorm2d(512),
nn.LeakyReLU(0.2, inplace=True) ]
# FCN classification layer
model += [nn.Conv2d(512, 1, 4, padding=1)]
self.model = nn.Sequential(*model)
def forward(self,x):
x = self.model(x)
# Average pooling and flatten
return F.avg_pool2d(x, x.size()[2:]).view(x.size()[0], -1).squeeze() #global avg pool