Skip to content

Update adv_image.py #52

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
yizhouf743 wants to merge 2 commits into
base: master
Choose a base branch
from
Open

Update adv_image.py #52

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
b42d36a
Update adv_image.py
yizhouf743 Jan 20, 2025
f764e23
Update adv_image.py
yizhouf743 Jan 20, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
18 changes: 15 additions & 3 deletions deepsecure/adv_image.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -17,13 +17,22 @@ def weights_init(m):
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0)


# Adversarial Image Generator class
class Adv_Gen:
def __init__(self,
device,
model_extractor,
generator,):

"""
Initialize the Adversarial Image Generator.

Args:
device (torch.device): The device (CPU/GPU) to run the model on.
model_extractor (nn.Module): Feature extractor model.
generator (nn.Module): Generator model to create adversarial images.
"""

self.device = device
self.model_extractor = model_extractor
self.generator = generator
Expand All @@ -32,15 +41,16 @@ def __init__(self,
self.ite = 0
#self.CELoss = nn.CrossEntropyLoss()

# move model and generator to pre-defined device
self.model_extractor.to(device)
#self.model_extractor.eval()

self.generator.to(device)

# initialize optimizers
self.optimizer_G = torch.optim.Adam(self.generator.parameters(),
lr=0.001)


# Create directories for saving models and adversarial images if they don't exist
if not os.path.exists(models_path):
os.makedirs(models_path)
if not os.path.exists(adv_img_path):
Expand All @@ -63,9 +73,11 @@ def train(self, train_dataloader, epochs):
for epoch in range(1, epochs+1):

if epoch == 200:
# Adjust netG's learning rate to 1e-4 at epoch 200
self.optimizer_G = torch.optim.Adam(self.netG.parameters(),
lr=0.0001)
if epoch == 400:
# Adjust netG's learning rate to 1e-5 at epoch 400
self.optimizer_G = torch.optim.Adam(self.netG.parameters(),
lr=0.00001)
loss_adv_sum = 0
Expand Down