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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
class Encoder(nn.Module):
def __init__(self, hdim=256):
super(Encoder, self).__init__()
self.hdim = hdim
self.main = nn.Sequential(
nn.Conv2d(3, 32, 5, 2, 2, bias=False),
nn.InstanceNorm2d(32, eps=0.001),
nn.LeakyReLU(0.2),
nn.AvgPool2d(2),
make_layer(_Residual_Block, 1, 32, 64),
nn.AvgPool2d(2),
make_layer(_Residual_Block, 1, 64, 128),
nn.AvgPool2d(2),
make_layer(_Residual_Block, 1, 128, 256),
nn.AvgPool2d(2),
make_layer(_Residual_Block, 1, 256, 512),
nn.AvgPool2d(2),
make_layer(_Residual_Block, 1, 512, 512)
)
# mu and logvar
self.fc = nn.Linear(512 * 4 * 4, 2 * hdim)
def forward(self, x):
z = self.main(x).view(x.size(0), -1)
z = self.fc(z)
mu, logvar = torch.split(z, split_size_or_sections=self.hdim, dim=-1)
return mu, logvar
class Encoder_s(nn.Module):
def __init__(self, hdim=256):
super(Encoder_s, self).__init__()
self.hdim = hdim
self.main = nn.Sequential(
nn.Conv2d(3, 32, 5, 2, 2, bias=False),
nn.InstanceNorm2d(32, eps=0.001),
nn.LeakyReLU(0.2),
nn.AvgPool2d(2),
make_layer(_Residual_Block, 1, 32, 64),
nn.AvgPool2d(2),
make_layer(_Residual_Block, 1, 64, 128),
nn.AvgPool2d(2),
make_layer(_Residual_Block, 1, 128, 256),
nn.AvgPool2d(2),
make_layer(_Residual_Block, 1, 256, 512),
nn.AvgPool2d(2),
make_layer(_Residual_Block, 1, 512, 512)
)
# mu and logvar
self.fc = nn.Linear(512 * 4 * 4, 4 * hdim)
# self.fc_at = nn.Linear(hdim, attack_type)
def forward(self, x):
z = self.main(x).view(x.size(0), -1)
z = self.fc(z)
mu, logvar, mu_a, logvar_a = torch.split(z, split_size_or_sections=self.hdim, dim=-1)
# a_type = self.fc_at(a_t)
return mu, logvar, mu_a, logvar_a
class Cls(nn.Module):
def __init__(self, hdim=256, attack_type=4):
super(Cls, self).__init__()
self.fc = nn.Linear(hdim, attack_type)
def forward(self, x):
a_cls = self.fc(x)
return a_cls
class Decoder(nn.Module):
def __init__(self, hdim=256):
super(Decoder, self).__init__()
self.fc = nn.Sequential(
nn.Linear(hdim, 512 * 4 * 4),
nn.ReLU(True)
)
self.main = nn.Sequential(
make_layer(_Residual_Block, 1, 512, 512),
nn.Upsample(scale_factor=2, mode='nearest'),
make_layer(_Residual_Block, 1, 512, 512),
nn.Upsample(scale_factor=2, mode='nearest'),
make_layer(_Residual_Block, 1, 512, 512),
nn.Upsample(scale_factor=2, mode='nearest'),
make_layer(_Residual_Block, 1, 512, 256),
nn.Upsample(scale_factor=2, mode='nearest'),
make_layer(_Residual_Block, 1, 256, 128),
nn.Upsample(scale_factor=2, mode='nearest'),
make_layer(_Residual_Block, 2, 128, 64),
nn.Upsample(scale_factor=2, mode='nearest'),
nn.Conv2d(64, 3 + 3, 5, 1, 2)
)
def forward(self, z):
z = z.view(z.size(0), -1)
y = self.fc(z)
x = y.view(z.size(0), -1, 4, 4)
img = torch.sigmoid(self.main(x))
return img
class Decoder_s(nn.Module):
def __init__(self, hdim=256):
super(Decoder_s, self).__init__()
self.fc = nn.Sequential(
nn.Linear(3 * hdim, 512 * 4 * 4),
nn.ReLU(True)
)
self.main = nn.Sequential(
make_layer(_Residual_Block, 1, 512, 512),
nn.Upsample(scale_factor=2, mode='nearest'),
make_layer(_Residual_Block, 1, 512, 512),
nn.Upsample(scale_factor=2, mode='nearest'),
make_layer(_Residual_Block, 1, 512, 512),
nn.Upsample(scale_factor=2, mode='nearest'),
make_layer(_Residual_Block, 1, 512, 256),
nn.Upsample(scale_factor=2, mode='nearest'),
make_layer(_Residual_Block, 1, 256, 128),
nn.Upsample(scale_factor=2, mode='nearest'),
make_layer(_Residual_Block, 2, 128, 64),
nn.Upsample(scale_factor=2, mode='nearest'),
nn.Conv2d(64, 3 + 3, 5, 1, 2)
)
def forward(self, z):
z = z.view(z.size(0), -1)
y = self.fc(z)
x = y.view(z.size(0), -1, 4, 4)
img = torch.sigmoid(self.main(x))
return img
class _Residual_Block(nn.Module):
def __init__(self, inc=64, outc=64, groups=1):
super(_Residual_Block, self).__init__()
if inc is not outc:
self.conv_expand = nn.Conv2d(in_channels=inc, out_channels=outc, kernel_size=1, stride=1, padding=0,
groups=1, bias=False)
else:
self.conv_expand = None
self.conv1 = nn.Conv2d(in_channels=inc, out_channels=outc, kernel_size=3, stride=1, padding=1, groups=groups,
bias=False)
self.bn1 = nn.InstanceNorm2d(outc, eps=0.001)
self.relu1 = nn.LeakyReLU(0.2, inplace=True)
self.conv2 = nn.Conv2d(in_channels=outc, out_channels=outc, kernel_size=3, stride=1, padding=1, groups=groups,
bias=False)
self.bn2 = nn.InstanceNorm2d(outc, eps=0.001)
self.relu2 = nn.LeakyReLU(0.2, inplace=True)
def forward(self, x):
if self.conv_expand is not None:
identity_data = self.conv_expand(x)
else:
identity_data = x
output = self.relu1(self.bn1(self.conv1(x)))
output = self.conv2(output)
output = self.relu2(self.bn2(torch.add(output, identity_data)))
return output
def make_layer(block, num_of_layer, inc=64, outc=64, groups=1):
if num_of_layer < 1:
num_of_layer = 1
layers = []
layers.append(block(inc=inc, outc=outc, groups=groups))
for _ in range(1, num_of_layer):
layers.append(block(inc=outc, outc=outc, groups=groups))
return nn.Sequential(*layers)
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