trojanvision.trainer.add_argument(parser)
trojanvision.marks.add_argument(parser)
trojanvision.attacks.add_argument(parser)
trojanvision.defenses.add_argument(parser)
args, unknown = parser.parse_known_args()
env = trojanvision.environ.create(**args.__dict__)
dataset = trojanvision.datasets.create(**args.__dict__)
model = trojanvision.models.create(dataset=dataset, **args.__dict__)
trainer = trojanvision.trainer.create(dataset=dataset, model=model, **args.__dict__)
mark = trojanvision.marks.create(dataset=dataset, **args.__dict__)
attack: BadNet = trojanvision.attacks.create(dataset=dataset, model=model, mark=mark, **args.__dict__)
defense: NeuralCleanse = trojanvision.defenses.create(dataset=dataset, model=model, attack=attack, **args.__dict__)
if env['verbose']:
summary(env=env, dataset=dataset, model=model, mark=mark, trainer=trainer, attack=attack, defense=defense)
simple_parser = argparse.ArgumentParser()
simple_parser.add_argument('--mark_source', default='defense')
simple_parser.add_argument('--unlearn_mode', default='batch')
args, unknown = simple_parser.parse_known_args()
mark_source: str = args.mark_source
unlearn_mode: str = args.unlearn_mode
if mark_source == 'attack':
mark_source = attack.name
elif mark_source in ['defense', defense.name]:
mark_source = defense.name
if mark_source == attack.name:
attack.load()
import argparse
import warnings
import os
warnings.filterwarnings("ignore")
if __name__ == '__main__':
parser = argparse.ArgumentParser()
trojanvision.environ.add_argument(parser)
trojanvision.datasets.add_argument(parser)
trojanvision.models.add_argument(parser)
args = parser.parse_args()
env = trojanvision.environ.create(**args.__dict__)
dataset = trojanvision.datasets.create(**args.__dict__)
model = trojanvision.models.create(dataset=dataset, **args.__dict__)
if env['verbose']:
summary(env=env, dataset=dataset, model=model)
import torch
import numpy as np
from sklearn import metrics
from trojanzoo.utils.data import dataset_to_list
from art.estimators.classification import PyTorchClassifier # type: ignore
classifier = PyTorchClassifier(
model=model._model,
loss=model.criterion,
input_shape=dataset.data_shape,
nb_classes=model.num_classes,
)
model._validate()
from art.attacks.inference.membership_inference import LabelOnlyDecisionBoundary as Attack # type: ignore
import warnings
warnings.filterwarnings("ignore")
if __name__ == '__main__':
parser = argparse.ArgumentParser()
trojanvision.environ.add_argument(parser)
trojanvision.datasets.add_argument(parser)
trojanvision.models.add_argument(parser)
trojanvision.marks.add_argument(parser)
trojanvision.attacks.add_argument(parser)
args = parser.parse_args()
env = trojanvision.environ.create(**args.__dict__)
dataset = trojanvision.datasets.create(**args.__dict__)
model = trojanvision.models.create(dataset=dataset, **args.__dict__)
mark = trojanvision.marks.create(dataset=dataset, **args.__dict__)
attack: BadNet = trojanvision.attacks.create(dataset=dataset,
model=model,
mark=mark,
**args.__dict__)
if env['verbose']:
summary(env=env,
dataset=dataset,
model=model,
mark=mark,
attack=attack)
attack.load()
attack.validate_fn()
import warnings
warnings.filterwarnings("ignore")
if __name__ == '__main__':
parser = argparse.ArgumentParser()
trojanvision.environ.add_argument(parser)
trojanvision.datasets.add_argument(parser)
trojanvision.models.add_argument(parser)
trojanvision.trainer.add_argument(parser)
trojanvision.attacks.add_argument(parser)
args = parser.parse_args()
env = trojanvision.environ.create(**args.__dict__)
dataset = trojanvision.datasets.create(**args.__dict__)
model = trojanvision.models.create(dataset=dataset, **args.__dict__)
trainer = trojanvision.trainer.create(dataset=dataset,
model=model,
**args.__dict__)
attack = trojanvision.attacks.create(dataset=dataset,
model=model,
**args.__dict__)
if env['verbose']:
summary(env=env,
dataset=dataset,
model=model,
train=trainer,
attack=attack)
attack.attack(**trainer)
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import trojanvision.environ
import trojanvision.datasets
import trojanvision.models
import trojanvision.trainer
from trojanvision.utils import summary
import argparse
import warnings
warnings.filterwarnings("ignore")
if __name__ == '__main__':
parser = argparse.ArgumentParser()
trojanvision.environ.add_argument(parser)
trojanvision.datasets.add_argument(parser)
trojanvision.models.add_argument(parser)
trojanvision.trainer.add_argument(parser)
args = parser.parse_args()
env = trojanvision.environ.create(**args.__dict__)
dataset = trojanvision.datasets.create(**args.__dict__)
model = trojanvision.models.create(dataset=dataset, **args.__dict__)
trainer = trojanvision.trainer.create(dataset=dataset, model=model, **args.__dict__)
if env['verbose']:
summary(env=env, dataset=dataset, model=model, trainer=trainer)
model._train(**trainer)
if self.attack_model_type not in ["nn", "rf", "gb"]:
raise ValueError(
"Illegal value for parameter `attack_model_type`.")
if __name__ == '__main__':
parser = argparse.ArgumentParser()
trojanvision.environ.add_argument(parser)
trojanvision.datasets.add_argument(parser)
trojanvision.models.add_argument(parser)
args = parser.parse_args()
env = trojanvision.environ.create(**args.__dict__)
dataset = trojanvision.datasets.create(**args.__dict__)
if env['verbose']:
summary(env=env, dataset=dataset)
api = create('/data/rbp5354/nats/NATS-tss-v1_0-3ffb9-full',
'tss',
fast_mode=True,
verbose=False)
# config = api.get_net_config(12, 'cifar10')
# params = api.get_net_param(12, 'cifar10', None)
# network = get_cell_based_tiny_net(config)
# network.load_state_dict(next(iter(params.values())))
f1_score_list: list[float] = []
accuracy_list: list[float] = []
recall_list: list[float] = []
