/
nets.py
98 lines (80 loc) · 3.48 KB
/
nets.py
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from torch import nn
import torch.nn.functional as F
class ResidualBlock(nn.Module):
def __init__(self, in_features):
super(ResidualBlock, self).__init__()
self.conv_block = nn.Sequential(
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))
def forward(self, x):
return x + self.conv_block(x)
class Generator(nn.Module):
def __init__(self, n_residual_blocks=9):
super(Generator, self).__init__()
# Initial convolution block
self.input_conv = nn.Sequential(
nn.ReflectionPad2d(3),
nn.Conv2d(3, 64, 7),
nn.InstanceNorm2d(64),
nn.ReLU(inplace=True))
# Downsampling
self.downsampling = nn.Sequential(
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
self.res = []
for _ in range(n_residual_blocks):
self.res += [ResidualBlock(256)]
self.residual_blocks = nn.Sequential(*self.res)
# Upsampling
self.upsampling = nn.Sequential(
nn.ConvTranspose2d(256, 128, 3, stride=2, padding=1, output_padding=1),
nn.InstanceNorm2d(128),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(128, 64, 3, stride=2, padding=1, output_padding=1),
nn.InstanceNorm2d(64),
nn.ReLU(inplace=True))
# Output layer
self.output = nn.Sequential(
nn.ReflectionPad2d(3),
nn.Conv2d(64, 3, 7),
nn.Tanh())
def forward(self, x):
x = self.input_conv(x)
x = self.downsampling(x)
x = self.residual_blocks(x)
x = self.upsampling(x)
x = self.output(x)
return x
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
# A bunch of convolutions one after another
self.features_extractor = nn.Sequential(
nn.Conv2d(3, 64, 4, stride=2, padding=1),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(64, 128, 4, stride=2, padding=1),
nn.InstanceNorm2d(128),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(128, 256, 4, stride=2, padding=1),
nn.InstanceNorm2d(256),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(256, 512, 4, padding=1),
nn.InstanceNorm2d(512),
nn.LeakyReLU(0.2, inplace=True))
# FCN classification layer
self.classificator = nn.Conv2d(512, 1, 4, padding=1)
def forward(self, x):
x = self.features_extractor(x)
x = self.classificator(x)
# Average pooling and flatten
return F.avg_pool2d(x, x.size()[2:]).view(x.size()[0], -1)