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import os
import argparse
import torch
import torch.nn
from torch.utils.data import TensorDataset
import torch.backends.cudnn as cudnn
class Generator(nn.Module):
def __init__(self, in_ch):
super(Generator, self).__init__()
self.conv1 = nn.Conv2d(in_ch, 64, 4, stride=2, padding=1)
self.bn1 = nn.BatchNorm2d(64)
self.conv2 = nn.Conv2d(64, 128, 4, stride=2, padding=1)
self.bn2 = nn.BatchNorm2d(128)
self.deconv3 = nn.ConvTranspose2d(128, 64, 4, stride=2, padding=1)
self.bn3 = nn.BatchNorm2d(64)
self.deconv4 = nn.ConvTranspose2d(64, in_ch, 4, stride=2, padding=1)
def forward(self, x):
h = F.leaky_relu(self.bn1(self.conv1(x)))
h = F.leaky_relu(self.bn2(self.conv2(h)))
h = F.leaky_relu(self.bn3(self.deconv3(h)))
h = F.tanh(self.deconv4(h))
return h
class Discriminator(nn.Module):
def __init__(self, in_ch):
super(Discriminator, self).__init__()
self.conv1 = nn.Conv2d(in_ch, 64, 3, stride=2)
self.conv2 = nn.Conv2d(64, 128, 3, stride=2)
self.bn2 = nn.BatchNorm2d(128)
self.conv3 = nn.Conv2d(128, 256, 3, stride=2)
self.bn3 = nn.BatchNorm2d(256)
if in_ch == 1:
self.fc4 = nn.Linear(1024, 1)
else:
self.fc4 = nn.Linear(2304, 1)
def forward(self, x):
h = F.leaky_relu(self.conv1(x))
h = F.leaky_relu(self.bn2(self.conv2(h)))
h = F.leaky_relu(self.bn3(self.conv3(h)))
h = F.sigmoid(self.fc4(h.view(h.size(0), -1)))
return h
def main(args):
#Initialize GAN model
G = Generator(in_ch = C).cuda()
D = Discriminator(in_ch = C).cuda()
#Initialize Generator
opt_G = torch.optim.Adam(G.parameters(), lr=lr, betas=(0.5, 0.999))
opt_D = torch.optim.Adam(D.parameters(), lr=lr, betas=(0.5, 0.999))
loss_bce = nn.BCELoss()
loss_mse = nn.MSELoss()
cudnn.benchmark = True
#Initialize DataLoader
train_data = torch.load("./adv_data.tar")
train_data = TensorDataset(train_data["normal"], train_data["adv"])
train_loader = torch.utils.data.DataLoader(train_data, batch_size=args.batch_size, shuffle=True)
#Start Training
for i in range(args.epochs):
G.eval()
x_fake = G(x_adv_temp).data
G.train()
gen_loss, dis_loss, n = 0, 0, 0
for x, x_adv in train_loader:
current_size = x.size(0)
x, x_adv = x.cuda(), x_adv.cuda()
#Train Discriminator
t_real = torch.ones(current_size).cuda()
t_fake = torch.zeros(current_size).cuda()
y_real = D(x).squeeze()
x_fake = G(x_adv)
y_fake = D(x_fake).squeeze()
loss_D = loss_bce(y_real, t_real) + loss_bce(y_fake, t_fake)
opt_D.zero_grad()
loss_D.backward()
opt_D.step()
# Train G
for _ in range(2):
x_fake = G(x_adv)
y_fake = D(x_fake).squeeze()
loss_G = args.alpha * loss_mse(x_fake, x) + args.beta * loss_bce(y_fake, t_real)
opt_G.zero_grad()
loss_G.backward()
opt_G.step()
gen_loss += loss_D.data[0] * x.size(0)
dis_loss += loss_G.data[0] * x.size(0)
n += x.size(0)
print("epoch:{}, LossG:{:.3f}, LossD:{:.3f}".format(i, gen_loss / n, dis_loss / n))
torch.save({"generator": G.state_dict(), "discriminator": D.state_dict()},
os.path.join(args.checkpoint, "{}.tar".format(i + 1)))
G.eval()
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--data", type=str, default="mnist")
parser.add_argument("--lr", type=float, default=0.0002)
parser.add_argument("--epochs", type=int, default=2)
parser.add_argument("--alpha", type=float, default=0.7)
parser.add_argument("--beta", type=float, default=0.3)
parser.add_argument("--checkpoint", type=str, default="./checkpoint/test")
args = parser.parse_args()
return args
if __name__ == "__main__":
get_args()
main(args)
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