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attack_FaceScrub.py
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attack_FaceScrub.py
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import os
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader, random_split
from torchvision.datasets import *
from torchvision.transforms.transforms import *
from torchvision.transforms.functional import *
from tqdm import tqdm
from torchplus.utils import Init, save_image2
from piq import SSIMLoss
if __name__ == "__main__":
batch_size = 128
train_epoches = 100
log_epoch = 4
class_num = 530
root_dir = "./logZZPMAIN.attack"
dataset_dir = "E:/datasets/FaceScrub"
target_pkl = "/path/to/target_classifier_face.pkl"
h = 64
w = 64
lr = 0.01
momentum = 0.9
weight_decay = 0.0005
init = Init(
seed=9970,
log_root_dir=root_dir,
sep=True,
backup_filename=__file__,
tensorboard=True,
comment=f"FaceScrub ATTACK mse11",
)
output_device = init.get_device()
writer = init.get_writer()
log_dir, model_dir = init.get_log_dir()
data_workers = 2
transform = Compose([Grayscale(num_output_channels=1), Resize((h, w)), ToTensor()])
ds = ImageFolder(root=dataset_dir, transform=transform)
ds_len = len(ds)
train_ds, test_ds = random_split(ds, [ds_len * 6 // 7, ds_len - ds_len * 6 // 7])
train_ds_len = len(train_ds)
test_ds_len = len(test_ds)
print(train_ds_len)
print(test_ds_len)
# for evaluate
train_dl = DataLoader(
dataset=train_ds,
batch_size=batch_size,
shuffle=False,
num_workers=data_workers,
drop_last=False,
pin_memory=True,
)
# for attack
test_dl = DataLoader(
dataset=test_ds,
batch_size=batch_size,
shuffle=True,
num_workers=data_workers,
drop_last=True,
pin_memory=True,
)
train_dl_len = len(train_dl)
test_dl_len = len(test_dl)
class Classifier(nn.Module):
def __init__(self, out_features):
super(Classifier, self).__init__()
self.out_features = out_features
self.conv1 = nn.Conv2d(1, 128, 3, 1, 1)
self.conv2 = nn.Conv2d(128, 256, 3, 1, 1)
self.conv3 = nn.Conv2d(256, 512, 3, 1, 1)
self.conv4 = nn.Conv2d(512, 1024, 3, 1, 1)
self.bn1 = nn.BatchNorm2d(128)
self.bn2 = nn.BatchNorm2d(256)
self.bn3 = nn.BatchNorm2d(512)
self.bn4 = nn.BatchNorm2d(1024)
self.mp1 = nn.MaxPool2d(2, 2)
self.mp2 = nn.MaxPool2d(2, 2)
self.mp3 = nn.MaxPool2d(2, 2)
self.mp4 = nn.MaxPool2d(2, 2)
self.relu1 = nn.ReLU()
self.relu2 = nn.ReLU()
self.relu3 = nn.ReLU()
self.relu4 = nn.ReLU()
self.fc1 = nn.Linear(16384, 2650)
self.dropout = nn.Dropout()
self.fc2 = nn.Linear(2650, self.out_features)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.mp1(x)
x = self.relu1(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.mp2(x)
x = self.relu2(x)
x = self.conv3(x)
x = self.bn3(x)
x = self.mp3(x)
x = self.relu3(x)
x = self.conv4(x)
x = self.bn4(x)
x = self.mp4(x)
x = self.relu4(x)
x = x.view(-1, 16384)
x = self.fc1(x)
x = self.dropout(x)
x = self.fc2(x)
return x
class PowerAmplification(nn.Module):
def __init__(
self, in_features: int, alpha: float = None, device=None, dtype=None
) -> None:
super(PowerAmplification, self).__init__()
factory_kwargs = {"device": device, "dtype": dtype}
self.in_features = in_features
if alpha is not None:
self.alpha = Parameter(torch.tensor([alpha], **factory_kwargs))
else:
self.alpha = Parameter(torch.rand(1, **factory_kwargs))
def forward(self, input: Tensor) -> Tensor:
alpha = self.alpha.expand(self.in_features)
return torch.pow(input, alpha)
class Inversion(nn.Module):
def __init__(self, in_channels):
super(Inversion, self).__init__()
self.in_channels = in_channels
self.deconv1 = nn.ConvTranspose2d(self.in_channels, 1024, 4, 1)
self.deconv2 = nn.ConvTranspose2d(1024, 512, 4, 2, 1)
self.deconv3 = nn.ConvTranspose2d(512, 256, 4, 2, 1)
self.deconv4 = nn.ConvTranspose2d(256, 128, 4, 2, 1)
self.deconv5 = nn.ConvTranspose2d(128, 1, 4, 2, 1)
self.bn1 = nn.BatchNorm2d(1024)
self.bn2 = nn.BatchNorm2d(512)
self.bn3 = nn.BatchNorm2d(256)
self.bn4 = nn.BatchNorm2d(128)
self.relu1 = nn.ReLU()
self.relu2 = nn.ReLU()
self.relu3 = nn.ReLU()
self.relu4 = nn.ReLU()
self.sigmod = nn.Sigmoid()
def forward(self, x):
x = x.view(-1, self.in_channels, 1, 1)
x = self.deconv1(x)
x = self.bn1(x)
x = self.relu1(x)
x = self.deconv2(x)
x = self.bn2(x)
x = self.relu2(x)
x = self.deconv3(x)
x = self.bn3(x)
x = self.relu3(x)
x = self.deconv4(x)
x = self.bn4(x)
x = self.relu4(x)
x = self.deconv5(x)
x = self.sigmod(x)
return x
target_classifier = Classifier(class_num).train(False).to(output_device)
target_amplification = (
PowerAmplification(class_num, 1 / 11).train(False).to(output_device)
)
myinversion = Inversion(class_num).train(True).to(output_device)
optimizer = optim.Adam(
myinversion.parameters(), lr=0.0002, betas=(0.5, 0.999), amsgrad=True
)
assert os.path.exists(target_pkl)
target_classifier.load_state_dict(
torch.load(open(target_pkl, "rb"), map_location=output_device)
)
target_classifier.requires_grad_(False)
target_amplification.requires_grad_(False)
for epoch_id in tqdm(range(1, train_epoches + 1), desc="Total Epoch"):
for i, (im, label) in enumerate(tqdm(test_dl, desc=f"epoch {epoch_id}")):
im = im.to(output_device)
label = label.to(output_device)
bs, c, h, w = im.shape
optimizer.zero_grad()
out = target_classifier.forward(im)
after_softmax = F.softmax(out, dim=-1)
after_softmax = target_amplification.forward(after_softmax)
rim = myinversion.forward(after_softmax)
mse = F.mse_loss(rim, im)
loss = mse
loss.backward()
optimizer.step()
if epoch_id % log_epoch == 0:
writer.add_scalar("loss", loss, epoch_id)
writer.add_scalar("mse", mse, epoch_id)
save_image2(im.detach(), f"{log_dir}/input/{epoch_id}.png")
save_image2(rim.detach(), f"{log_dir}/output/{epoch_id}.png")
with open(
os.path.join(model_dir, f"myinversion_{epoch_id}.pkl"), "wb"
) as f:
torch.save(myinversion.state_dict(), f)
if epoch_id % log_epoch == 0:
with torch.no_grad():
myinversion.eval()
r = 0
ssimloss = 0
mseloss = 0
for i, (im, label) in enumerate(tqdm(train_dl, desc=f"priv")):
r += 1
im = im.to(output_device)
label = label.to(output_device)
bs, c, h, w = im.shape
out = target_classifier.forward(im)
after_softmax = F.softmax(out, dim=-1)
after_softmax = target_amplification.forward(after_softmax)
rim = myinversion.forward(after_softmax)
ssim = SSIMLoss()(rim, im)
mse = F.mse_loss(rim, im)
ssimloss += ssim
mseloss += mse
ssimlossavg = ssimloss / r
mselossavg = mseloss / r
writer.add_scalar("priv ssim", ssimlossavg, epoch_id)
writer.add_scalar("priv mse", mselossavg, epoch_id)
r = 0
ssimloss = 0
mseloss = 0
for i, (im, label) in enumerate(tqdm(test_dl, desc=f"aux")):
r += 1
im = im.to(output_device)
label = label.to(output_device)
bs, c, h, w = im.shape
out = target_classifier.forward(im)
after_softmax = F.softmax(out, dim=-1)
after_softmax = target_amplification.forward(after_softmax)
rim = myinversion.forward(after_softmax)
ssim = SSIMLoss()(rim, im)
mse = F.mse_loss(rim, im)
ssimloss += ssim
mseloss += mse
ssimlossavg = ssimloss / r
mselossavg = mseloss / r
writer.add_scalar("aux ssim", ssimlossavg, epoch_id)
writer.add_scalar("aux mse", mselossavg, epoch_id)
writer.close()