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models.py
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models.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import random
class Generator(nn.Module):
def __init__(self):
super(Generator, self).__init__()
self.main = nn.Sequential(
# Initial convolution block
nn.ReflectionPad2d(3),
nn.Conv2d(3, 64, 7),
nn.InstanceNorm2d(64),
nn.ReLU(inplace=True),
# Downsampling
nn.Conv2d(64, 128, 3, stride=2, padding=1),
nn.InstanceNorm2d(128),
nn.ReLU(inplace=True),
nn.Conv2d(128, 256, 3, stride=2, padding=1),
nn.InstanceNorm2d(256),
nn.ReLU(inplace=True),
# Residual blocks
ResidualBlock(256),
ResidualBlock(256),
ResidualBlock(256),
ResidualBlock(256),
ResidualBlock(256),
ResidualBlock(256),
# Upsampling
nn.Upsample(scale_factor = 2, mode='nearest'),
nn.ReflectionPad2d(1),
nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=0),
nn.InstanceNorm2d(128),
nn.ReLU(inplace=True),
nn.Upsample(scale_factor = 2, mode='nearest'),
nn.ReflectionPad2d(1),
nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=0),
nn.InstanceNorm2d(64),
nn.ReLU(inplace=True),
# Output layer
nn.ReflectionPad2d(3),
nn.Conv2d(64, 3, 7),
nn.Tanh()
)
def forward(self, x):
out = self.main(x)
return out
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
self.main = nn.Sequential(
nn.Conv2d(3, 64, 5, stride=2, padding=1),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(64, 128, 5, stride=2, padding=1),
nn.InstanceNorm2d(128),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(128, 256, 5, stride=2, padding=1),
nn.InstanceNorm2d(256),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(256, 512, 5, stride=2, padding=0),
nn.InstanceNorm2d(512),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(512, 1, 5, padding=0)
)
def forward(self, x):
out = self.main(x)
out2 = torch.flatten(out)
return out2
class ResidualBlock(nn.Module):
def __init__(self, in_channels):
super(ResidualBlock, self).__init__()
self.res = nn.Sequential(nn.ReflectionPad2d(1),
nn.Conv2d(in_channels, in_channels, 3),
nn.InstanceNorm2d(in_channels),
nn.ReLU(inplace=True),
nn.ReflectionPad2d(1),
nn.Conv2d(in_channels, in_channels, 3),
nn.InstanceNorm2d(in_channels))
def forward(self, x):
return x + self.res(x)
# Initialize the weights of the generator
def weight_init_g(layer):
if type(layer) == nn.ConvTranspose2d:
nn.init.normal_(layer.weight.data, 0.0, 0.02)
elif type(layer) == nn.InstanceNorm2d:
nn.init.normal_(layer.weight.data, 1.0, 0.02)
nn.init.constant_(layer.bias.data, 0.0)
# end if
# end weight_init_generator
# Initialize the weights of the discriminator
def weight_init_d(layer):
if type(layer) == nn.Conv2d:
nn.init.normal_(layer.weight.data, 0.0, 0.02)
elif type(layer) == nn.InstanceNorm2d:
nn.init.normal_(layer.weight.data, 1.0, 0.02)
nn.init.constant_(layer.bias.data, 0.0)
# end if
# end weight_init_generator
def weights_init(m):
classname = m.__class__.__name__
if classname.find("Conv") != -1:
torch.nn.init.normal_(m.weight, 0.0, 0.02)
elif classname.find("BatchNorm") != -1:
torch.nn.init.normal_(m.weight, 1.0, 0.02)
torch.nn.init.zeros_(m.bias)
class ImageBuffer():
def __init__(self):
self.data = []
self.max_size = 50
def push_and_pop(self, data):
to_return = []
for el in data.data:
el = torch.unsqueeze(el, 0)
if len(self.data) < self.max_size:
self.data.append(el)
to_return.append(el)
else:
if random.uniform(0, 1) > 0.5:
i = random.randint(0, self.max_size - 1)
to_return.append(self.data[i].clone())
self.data[i] = el
else:
to_return.append(el)
return torch.cat(to_return)