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refactor(examples): standardize all pipelines to use load_or_train #81

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33 changes: 15 additions & 18 deletions examples/attack_pipelines/run_evasion.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
import sys

sys.path.append("../../")

import argparse
import logging
from pathlib import Path
Expand All @@ -14,6 +15,7 @@
get_accuracy,
initialize_model,
load_data,
load_or_train,
train_classifier,
)

Expand All @@ -31,7 +33,7 @@ def parse_args() -> argparse.Namespace:
"--dataset",
type=str,
default="celeba",
help="Options: cifar10, fmnist, lfw, census, celeba, cifar100.",
help="Options: cifar10, cifar100, fmnist, mnist, lfw, census, celeba, utkface.",
)
parser.add_argument(
"--model", type=str, default="vgg", help="Options: resnet, vgg, cnn, linearnet."
Expand All @@ -54,7 +56,7 @@ def parse_args() -> argparse.Namespace:
parser.add_argument(
"--device",
type=str,
default=torch.device(f"cuda:{0}" if torch.cuda.is_available() else "cpu"),
default="cuda:0" if torch.cuda.is_available() else "cpu",
help="Device on which to run PyTorch",
)
parser.add_argument(
Expand Down Expand Up @@ -93,30 +95,25 @@ def main(args: argparse.Namespace) -> None:
# Set up filename and directories to save/load models
models_path = root_dir / "saved_models"
filename = f"{args.dataset}_{args.model}_{args.model_capacity}_{args.training_size * 100}_{args.batch_size}_{args.epochs}_{args.exp_id}.pt"
target_model_path = models_path / "target"
target_model_filename = target_model_path / filename
target_model_filename = models_path / "target" / filename

# Train or Load Target Model
criterion = torch.nn.CrossEntropyLoss()

if target_model_filename.exists():
log.info("Target model loaded from %s", target_model_filename)
target_model = torch.load(target_model_filename).to(args.device)
optimizer = torch.optim.Adam(target_model.parameters(), lr=1e-3)
else:
log.info("Training target model")
target_model = initialize_model(
def _init_target():
return initialize_model(
args.model, args.model_capacity, data.num_features, data.num_classes, log
).to(args.device)
optimizer = torch.optim.Adam(target_model.parameters(), lr=1e-3)
target_model = train_classifier(
target_model, train_loader, criterion, optimizer, args.epochs, args.device

def _train_target(model):
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
return train_classifier(
model, train_loader, criterion, optimizer, args.epochs, args.device
)
log.info("Target model trained")

# Save model
create_dir(target_model_path, log)
torch.save(target_model, target_model_filename)
target_model = load_or_train(
target_model_filename, _init_target, _train_target, log, "target model"
)

test_accuracy_target = get_accuracy(target_model, test_loader, args.device)
log.info("Test accuracy of target model: %s", test_accuracy_target)
Expand Down
35 changes: 16 additions & 19 deletions examples/attack_pipelines/run_membership_inference.py
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Open in desktop
Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
import sys

sys.path.append("../../")

import argparse
import logging
from pathlib import Path
Expand All @@ -16,6 +17,7 @@
get_accuracy,
initialize_model,
load_data,
load_or_train,
train_classifier,
)

Expand Down Expand Up @@ -56,15 +58,15 @@ def parse_args() -> argparse.Namespace:
parser.add_argument(
"--device",
type=str,
default=torch.device(f"cuda:{0}" if torch.cuda.is_available() else "cpu"),
default="cuda:0" if torch.cuda.is_available() else "cpu",
help="Device on which to run PyTorch",
)
parser.add_argument(
"--exp_id", type=int, default=0, help="Used as a random seed for experiments."
)
parser.add_argument(
"--pkeep",
type=int,
type=float,
default=0.5,
help="Proportion of data to keep for training.",
)
Expand All @@ -81,7 +83,7 @@ def main(args: argparse.Namespace) -> None:
log_dir = root_dir / "logs"
create_dir(log_dir)
logging.basicConfig(
level=logging.INFO, filename=log_dir / "evasion.log", filemode="w"
level=logging.INFO, filename=log_dir / "membership_inference.log", filemode="w"
)
log = logging.getLogger("All")
log.addHandler(logging.StreamHandler())
Expand Down Expand Up @@ -113,30 +115,25 @@ def main(args: argparse.Namespace) -> None:
# Set up filename and directories to save/load models
models_path = root_dir / "saved_models"
filename = f"{args.dataset}_{args.model}_{args.model_capacity}_{args.training_size * 100}_{args.batch_size}_{args.epochs}_{args.exp_id}.pt"
target_model_path = models_path / "target"
target_model_filename = target_model_path / filename
target_model_filename = models_path / "target" / filename

# Train or Load Target Model
criterion = torch.nn.CrossEntropyLoss()

if target_model_filename.exists():
log.info("Target model loaded from %s", target_model_filename)
target_model = torch.load(target_model_filename).to(args.device)
optimizer = torch.optim.Adam(target_model.parameters(), lr=1e-3)
else:
log.info("Training target model")
target_model = initialize_model(
def _init_target():
return initialize_model(
args.model, args.model_capacity, data.num_features, data.num_classes, log
).to(args.device)
optimizer = torch.optim.Adam(target_model.parameters(), lr=1e-3)
target_model = train_classifier(
target_model, train_loader, criterion, optimizer, args.epochs, args.device

def _train_target(model):
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
return train_classifier(
model, train_loader, criterion, optimizer, args.epochs, args.device
)
log.info("Target model trained")

# Save model
create_dir(target_model_path, log)
torch.save(target_model, target_model_filename)
target_model = load_or_train(
target_model_filename, _init_target, _train_target, log, "target model"
)

test_accuracy_target = get_accuracy(target_model, test_loader, args.device)
log.info("Test accuracy of target model: %s", test_accuracy_target)
Expand Down
74 changes: 44 additions & 30 deletions examples/defense_pipelines/run_adversarial_training.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,19 +1,22 @@
import sys

sys.path.append("../../")

import argparse
import logging
from pathlib import Path

import torch
from torch.utils.data import DataLoader

from amulet.evasion.attacks import EvasionPGD
from amulet.evasion.defenses import AdversarialTrainingPGD
from amulet.utils import (
create_dir,
get_accuracy,
initialize_model,
load_data,
load_or_train,
train_classifier,
)

Expand All @@ -31,7 +34,7 @@ def parse_args() -> argparse.Namespace:
"--dataset",
type=str,
default="celeba",
help="Options: cifar10, fmnist, lfw, census, celeba.",
help="Options: cifar10, cifar100, fmnist, mnist, lfw, census, celeba, utkface.",
)
parser.add_argument(
"--model", type=str, default="vgg", help="Options: vgg, linearnet."
Expand All @@ -54,7 +57,7 @@ def parse_args() -> argparse.Namespace:
parser.add_argument(
"--device",
type=str,
default=torch.device(f"cuda:{0}" if torch.cuda.is_available() else "cpu"),
default="cuda:0" if torch.cuda.is_available() else "cpu",
help="Device on which to run PyTorch",
)
parser.add_argument(
Expand Down Expand Up @@ -93,45 +96,41 @@ def main(args: argparse.Namespace) -> None:
# Set up filename and directories to save/load models
models_path = root_dir / "saved_models"
filename = f"{args.dataset}_{args.model}_{args.model_capacity}_{args.training_size * 100}_{args.batch_size}_{args.epochs}_{args.exp_id}.pt"
target_model_path = models_path / "target"
target_model_filename = target_model_path / filename
target_model_filename = models_path / "target" / filename

# Train or Load Target Model
criterion = torch.nn.CrossEntropyLoss()

if target_model_filename.exists():
log.info("Target model loaded from %s", target_model_filename)
target_model = torch.load(target_model_filename).to(args.device)
optimizer = torch.optim.Adam(target_model.parameters(), lr=1e-3)
else:
log.info("Training target model")
target_model = initialize_model(
def _init_target():
return initialize_model(
args.model, args.model_capacity, data.num_features, data.num_classes, log
).to(args.device)
optimizer = torch.optim.Adam(target_model.parameters(), lr=1e-3)
target_model = train_classifier(
target_model, train_loader, criterion, optimizer, args.epochs, args.device

def _train_target(model):
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
return train_classifier(
model, train_loader, criterion, optimizer, args.epochs, args.device
)
log.info("Target model trained")

# Save model
create_dir(target_model_path, log)
torch.save(target_model, target_model_filename)
target_model = load_or_train(
target_model_filename, _init_target, _train_target, log, "target model"
)

test_accuracy_target = get_accuracy(target_model, test_loader, args.device)
log.info("Test accuracy of target model: %s", test_accuracy_target)

# Train or load model with Adversarial Training
defended_model_path = (
models_path / "adversarial_training" / f"epsilon_{args.epsilon}"
defended_model_filename = (
models_path / "adversarial_training" / f"epsilon_{args.epsilon}" / filename
)
defended_model_filename = defended_model_path / filename

if defended_model_filename.exists():
log.info("Defended model loaded from %s", defended_model_filename)
defended_model = torch.load(defended_model_filename)
else:
log.info("Retraining Model with Adversarial Training")
def _init_defended():
return initialize_model(
args.model, args.model_capacity, data.num_features, data.num_classes, log
).to(args.device)

def _train_defended(_model):
optimizer = torch.optim.Adam(target_model.parameters(), lr=1e-3)
adv_training = AdversarialTrainingPGD(
target_model,
criterion,
Expand All @@ -141,15 +140,30 @@ def main(args: argparse.Namespace) -> None:
args.epochs,
args.epsilon,
)
defended_model = adv_training.train_robust()
return adv_training.train_robust()

# Save model
create_dir(defended_model_path, log)
torch.save(defended_model, defended_model_filename)
defended_model = load_or_train(
defended_model_filename, _init_defended, _train_defended, log, "defended model"
)

test_accuracy_defended = get_accuracy(defended_model, test_loader, args.device)
log.info("Test accuracy of defended model: %s", test_accuracy_defended)

# Show the defense benefit: compare adversarial accuracy before and after
evasion = EvasionPGD(
target_model, test_loader, args.device, args.batch_size, args.epsilon
)
adv_loader = evasion.attack()
adv_accuracy_target = get_accuracy(target_model, adv_loader, args.device)
log.info("Adversarial accuracy (undefended): %s", adv_accuracy_target)

evasion_def = EvasionPGD(
defended_model, test_loader, args.device, args.batch_size, args.epsilon
)
adv_loader_def = evasion_def.attack()
adv_accuracy_defended = get_accuracy(defended_model, adv_loader_def, args.device)
log.info("Adversarial accuracy (defended): %s", adv_accuracy_defended)


if __name__ == "__main__":
args = parse_args()
Expand Down
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