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# DCGAN-like generator and discriminator
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import Parameter
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class SpectralNorm(nn.Module):
def __init__(self, module, name="weight", power_iterations=1):
super(SpectralNorm, self).__init__()
self.module = module
self.name = name
self.power_iterations = power_iterations
if not self._made_params():
self._make_params()
def _update_u_v(self):
u = getattr(self.module, self.name + "_u")
v = getattr(self.module, self.name + "_v")
w = getattr(self.module, self.name + "_bar")
height = w.data.shape[0]
for _ in range(self.power_iterations):
v.data = l2normalize(torch.mv(torch.t(w.view(height, -1).data), u.data))
u.data = l2normalize(torch.mv(w.view(height, -1).data, v.data))
sigma = u.dot(w.view(height, -1).mv(v))
setattr(self.module, self.name, w / sigma.expand_as(w))
def _made_params(self):
try:
u = getattr(self.module, self.name + "_u")
v = getattr(self.module, self.name + "_v")
w = getattr(self.module, self.name + "_bar")
return True
except AttributeError:
return False
def _make_params(self):
w = getattr(self.module, self.name)
height = w.data.shape[0]
width = w.view(height, -1).data.shape[1]
u = Parameter(w.data.new(height).normal_(0, 1), requires_grad=False)
v = Parameter(w.data.new(width).normal_(0, 1), requires_grad=False)
u.data = l2normalize(u.data)
v.data = l2normalize(v.data)
w_bar = Parameter(w.data)
del self.module._parameters[self.name]
self.module.register_parameter(self.name + "_u", u)
self.module.register_parameter(self.name + "_v", v)
self.module.register_parameter(self.name + "_bar", w_bar)
def forward(self, *args):
self._update_u_v()
return self.module.forward(*args)
class Generator(nn.Module):
def __init__(self, z_dim):
super(Generator, self).__init__()
self.z_dim = z_dim
self.model = nn.Sequential(
nn.ConvTranspose2d(z_dim, 512, 4, stride=1),
nn.InstanceNorm2d(512),
nn.ReLU(),
nn.ConvTranspose2d(512, 256, 4, stride=2, padding=(1, 1)),
nn.InstanceNorm2d(256),
nn.ReLU(),
nn.ConvTranspose2d(256, 128, 4, stride=2, padding=(1, 1)),
nn.InstanceNorm2d(128),
nn.ReLU(),
nn.ConvTranspose2d(128, 64, 4, stride=2, padding=(1, 1)),
nn.InstanceNorm2d(64),
nn.ReLU(),
nn.ConvTranspose2d(64, channels, 3, stride=1, padding=(1, 1)),
nn.Tanh(),
)
def forward(self, z):
return self.model(z.view(-1, self.z_dim, 1, 1))
channels = 3
leak = 0.1
w_g = 4
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
self.conv1 = SpectralNorm(nn.Conv2d(channels, 64, 3, stride=1, padding=(1, 1)))
self.conv2 = SpectralNorm(nn.Conv2d(64, 64, 4, stride=2, padding=(1, 1)))
self.conv3 = SpectralNorm(nn.Conv2d(64, 128, 3, stride=1, padding=(1, 1)))
self.conv4 = SpectralNorm(nn.Conv2d(128, 128, 4, stride=2, padding=(1, 1)))
self.conv5 = SpectralNorm(nn.Conv2d(128, 256, 3, stride=1, padding=(1, 1)))
self.conv6 = SpectralNorm(nn.Conv2d(256, 256, 4, stride=2, padding=(1, 1)))
self.conv7 = SpectralNorm(nn.Conv2d(256, 256, 3, stride=1, padding=(1, 1)))
self.conv8 = SpectralNorm(nn.Conv2d(256, 512, 4, stride=2, padding=(1, 1)))
self.fc = SpectralNorm(nn.Linear(w_g * w_g * 512, 1))
def forward(self, x):
m = x
m = nn.LeakyReLU(leak)(self.conv1(m))
m = nn.LeakyReLU(leak)(nn.InstanceNorm2d(64)(self.conv2(m)))
m = nn.LeakyReLU(leak)(nn.InstanceNorm2d(128)(self.conv3(m)))
m = nn.LeakyReLU(leak)(nn.InstanceNorm2d(128)(self.conv4(m)))
m = nn.LeakyReLU(leak)(nn.InstanceNorm2d(256)(self.conv5(m)))
m = nn.LeakyReLU(leak)(nn.InstanceNorm2d(256)(self.conv6(m)))
m = nn.LeakyReLU(leak)(nn.InstanceNorm2d(256)(self.conv7(m)))
m = nn.LeakyReLU(leak)(self.conv8(m))
return self.fc(m.view(-1, w_g * w_g * 512))
class Self_Attention(nn.Module):
"""Self attention Layer"""
def __init__(self, in_dim):
super(Self_Attention, self).__init__()
self.chanel_in = in_dim
self.query_conv = SpectralNorm(nn.Conv2d(in_channels=in_dim, out_channels=in_dim // 1, kernel_size=1))
self.key_conv = SpectralNorm(nn.Conv2d(in_channels=in_dim, out_channels=in_dim // 1, kernel_size=1))
self.value_conv = SpectralNorm(nn.Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=1))
self.gamma = nn.Parameter(torch.zeros(1))
self.softmax = nn.Softmax(dim=-1) #
def forward(self, x):
"""
inputs :
x : input feature maps( B X C X W X H)
returns :
out : self attention value + input feature
attention: B X N X N (N is Width*Height)
"""
m_batchsize, C, width, height = x.size()
proj_query = self.query_conv(x).view(m_batchsize, -1, width * height).permute(0, 2, 1) # B X CX(N)
proj_key = self.key_conv(x).view(m_batchsize, -1, width * height) # B X C x (*W*H)
energy = torch.bmm(proj_query, proj_key) # transpose check
attention = self.softmax(energy) # BX (N) X (N)
proj_value = self.value_conv(x).view(m_batchsize, -1, width * height) # B X C X N
out = torch.bmm(proj_value, attention.permute(0, 2, 1))
out = out.view(m_batchsize, C, width, height)
out = self.gamma * out + x
return out
class Discriminator_x64_224(nn.Module):
"""
Discriminative Network
"""
def __init__(self, in_size=6, ndf=64):
super(Discriminator_x64_224, self).__init__()
self.in_size = in_size
self.ndf = ndf
self.layer1 = nn.Sequential(SpectralNorm(nn.Conv2d(self.in_size, self.ndf, 4, 2, 1)), nn.LeakyReLU(0.2, inplace=True))
self.layer2 = nn.Sequential(
SpectralNorm(nn.Conv2d(self.ndf, self.ndf, 4, 2, 1)),
nn.InstanceNorm2d(self.ndf),
nn.LeakyReLU(0.2, inplace=True),
)
self.attention = Self_Attention(self.ndf)
self.layer3 = nn.Sequential(
SpectralNorm(nn.Conv2d(self.ndf, self.ndf * 2, 4, 2, 1)),
nn.InstanceNorm2d(self.ndf * 2),
nn.LeakyReLU(0.2, inplace=True),
)
self.layer4 = nn.Sequential(
SpectralNorm(nn.Conv2d(self.ndf * 2, self.ndf * 4, 4, 2, 1)),
nn.InstanceNorm2d(self.ndf * 4),
nn.LeakyReLU(0.2, inplace=True),
)
self.layer5 = nn.Sequential(
SpectralNorm(nn.Conv2d(self.ndf * 4, self.ndf * 8, 4, 2, 1)),
nn.InstanceNorm2d(self.ndf * 8),
nn.LeakyReLU(0.2, inplace=True),
)
self.layer6 = nn.Sequential(
SpectralNorm(nn.Conv2d(self.ndf * 8, self.ndf * 16, 4, 2, 1)),
nn.InstanceNorm2d(self.ndf * 16),
nn.LeakyReLU(0.2, inplace=True),
)
self.last = SpectralNorm(nn.Conv2d(self.ndf * 16, 1, [3, 3], 1, 0))
def forward(self, input):
feature1 = self.layer1(input)
feature2 = self.layer2(feature1)
feature_attention = self.attention(feature2)
feature3 = self.layer3(feature_attention)
feature4 = self.layer4(feature3)
feature5 = self.layer5(feature4)
feature6 = self.layer6(feature5)
output = self.last(feature6)
output = F.avg_pool2d(output, output.size()[2:]).view(output.size()[0], -1)
return output, feature4
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