def test_can_patch_mnist_dataset():
dataset = datasets.mnist()
a_patch = patch.Patch('block',
proportion=0.05,
input_shape=dataset.input_shape,
dynamic_mask=False,
dynamic_pattern=False)
objective = util.make_objective_class(0, dataset.num_classes)
objective = util.make_objective_class(0, dataset.num_classes)
fraction = 0.1
poisoned = dataset.poison(objective, a_patch, fraction)
train_raw_poisoned = dataset.x_train[poisoned.train_poisoned_idx]
train_patched_poisoned = poisoned.x_train[poisoned.train_poisoned_idx]
# training set: verify that the indexes that are supposed to be patched
# indeed have the patch
_ = a_patch.apply(train_raw_poisoned)
np.testing.assert_array_equal(_, train_patched_poisoned)
test_raw_poisoned = dataset.x_test[poisoned.test_poisoned_idx]
test_patched_poisoned = poisoned.x_test[poisoned.test_poisoned_idx]
# test set: verify that the indexes that are supposed to be patched
# indeed have the patch
_ = a_patch.apply(test_raw_poisoned)
np.testing.assert_array_equal(_, test_patched_poisoned)
# training set: verify that the indexes that are NOT supposed to be patched
# indeed DO NOT have the patch
train_raw_nonpoisoned = dataset.x_train[~poisoned.train_poisoned_idx]
train_patched_nonpoisoned = poisoned.x_train[~poisoned.train_poisoned_idx]
np.testing.assert_array_equal(train_raw_nonpoisoned,
train_patched_nonpoisoned)
# test set: verify that the indexes that are NOT supposed to be patched
# indeed DO NOT have the patch
test_raw_nonpoisoned = dataset.x_test[~poisoned.test_poisoned_idx]
test_patched_nonpoisoned = poisoned.x_test[~poisoned.test_poisoned_idx]
np.testing.assert_array_equal(test_raw_nonpoisoned,
test_patched_nonpoisoned)
def test_can_unpickle_mnist_poisoned_dataset(temporary_filepath):
dataset = datasets.mnist()
a_patch = patch.Patch('block',
proportion=0.05,
input_shape=dataset.input_shape,
dynamic_mask=False,
dynamic_pattern=False)
objective = util.make_objective_class(0, dataset.num_classes)
objective = util.make_objective_class(0, dataset.num_classes)
fraction = 0.1
poisoned = dataset.poison(objective, a_patch, fraction)
poisoned.pickle(temporary_filepath, only_test_data=True)
unpickled = datasets.Dataset.from_pickle(temporary_filepath)
# check that the data is still the same
np.testing.assert_array_equal(poisoned.x_test, unpickled.x_test)
np.testing.assert_array_equal(poisoned.y_test, unpickled.y_test)
np.testing.assert_array_equal(poisoned.y_test_cat, unpickled.y_test_cat)
def test_can_run_mnist_experiment():
clean = datasets.mnist(100)
objective_class = 5
p = patch.Patch('block',
proportion=0.01,
input_shape=clean.input_shape,
dynamic_mask=False,
dynamic_pattern=False)
objective = util.make_objective_class(objective_class, clean.num_classes)
patched = clean.poison(objective, p, fraction=0.1)
trainer = partial(train.mnist_cnn, model_loader=models.mnist_cnn, epochs=1)
experiment.run(trainer, patched, [accuracy_score])
if args.model:
set_root_folder('/home/Edu/data')
model, poisoned_dataset, metadata = experiment.load(args.model)
dataset_name=metadata['dataset']['name'].lower()
clean_dataset = datasets.__dict__[dataset_name]()
klass = metadata['dataset']['poison_settings']['objective_class_cat']
elif args.fraction > -1:
clean_dataset = datasets.__dict__[args.dataset]()
patch_args = eval('dict('+args.patchargs+')')
patch_args['input_shape'] = clean_dataset.input_shape
klass = 0
if args.fraction > 0:
Patch = patch.__dict__[args.patchclass]
a_patch = Patch(**patch_args)
objective = util.make_objective_class(klass, clean_dataset.num_classes)
dataset_poisoned = clean_dataset.poison(objective, a_patch, args.fraction)
if args.pictures:
f,ax = plt.subplots(3,2)
idx=0
for i in range(3):
while not dataset_poisoned.train_poisoned_idx[idx]:
idx += 1
if dataset_poisoned.input_shape[-1] == 1:
ax[i][0].imshow(clean_dataset.x_train[idx,:,:,0], cmap=cm.gray_r)
ax[i][1].imshow(dataset_poisoned.x_train[idx,:,:,0], cmap=cm.gray_r)
else:
ax[i][0].imshow(clean_dataset.x_train[idx])
ax[i][1].imshow(dataset_poisoned.x_train[idx])
idx += 1
plt.show()
def _experiment(config, group_name=None, skip=0):
"""Run an experiment
"""
with open(config) as file:
CONFIG = yaml.load(file)
#################
# Configuration #
#################
ROOT_FOLDER = expanduser(CONFIG['root_folder'])
# load logging config file
now = datetime.datetime.now()
name = now.strftime('%d-%b-%Y@%H-%M-%S')
log_path = Path(ROOT_FOLDER, '{}.log'.format(name))
logging_config = yaml.load(logger_config)
logging_config['handlers']['file']['filename'] = log_path
# configure logging
logging.config.dictConfig(logging_config)
# instantiate logger
logger = logging.getLogger(__name__)
# root folder (experiments will be saved here)
set_root_folder(ROOT_FOLDER)
# db configuration (experiments metadata will be saved here)
set_db_conf(expanduser(CONFIG['db_config']))
logger.info('trojan_defender version: %s', trojan_defender.__version__)
logger.info('group name: %s', group_name)
logger.info('Dataset: %s', CONFIG['dataset'])
##################################
# Functions depending on dataset #
##################################
if CONFIG['dataset'] == 'mnist':
train_fn = train.train
batch_size = 128
dataset = datasets.mnist()
elif CONFIG['dataset'] == 'cifar10':
train_fn = train.train
batch_size = 32
dataset = datasets.cifar10()
else:
raise ValueError('config.dataset must be mnist or cifar 10')
model_loader = getattr(models, CONFIG['architecture'])
#########################
# Experiment parameters #
#########################
input_shape = dataset.input_shape
epochs = CONFIG['epochs']
objective = util.make_objective_class(CONFIG['objective_class'],
dataset.num_classes)
# list of metrics to evaluate
the_metrics = [getattr(metrics, metric) for metric in CONFIG['metrics']]
# trainer object
trainer = partial(train_fn,
model_loader=model_loader,
batch_size=batch_size,
epochs=epochs)
###################################
# Experiment parameters: patching #
###################################
p = CONFIG['patch']
patching_parameters = list(
product(p['types'], p['proportions'], p['dynamic_masks'],
p['dynamic_pattern'])) * p['trials']
patches = [
Patch(type_, proportion, input_shape, dynamic_mask, dynamic_pattern)
for type_, proportion, dynamic_mask, dynamic_pattern in
patching_parameters
]
poison_parameters = list(product(patches, CONFIG['poison_fractions']))
# generate poisoned datasets from the parameters
patching_poisoned = (dataset.poison(objective, a_patch, fraction=fraction)
for a_patch, fraction in poison_parameters)
datasets_all = chain([dataset], patching_poisoned)
n = len(poison_parameters) + 1
for i, a_dataset in enumerate(datasets_all, 1):
logger.info('Training %i/%i', i, n)
if not trojan_defender.TESTING:
if i >= skip:
trojan_defender_experiment.run(trainer, a_dataset, the_metrics,
group_name)
else:
logger.info('Skipping %i...', i)
else:
logger.info('Testing, skipping training...')