logger = logging.getLogger(__name__)
# Nota: Molto spesso, il threshold di rifiuto influenza la distanza ottenuta anche quando non influenza
# il tasso di successo dell'evasion. Questo è perché cambiare il threshold cambia la loss, cambiando
# quindi il comportamento dell'attacco
# Supporto per metrica diversa?
@click.command()
@click.argument('domain', type=click.Choice(parsing.domains))
@click.argument('architecture', type=click.Choice(parsing.architectures))
@click.argument('dataset')
@click.argument('counter_attacks',
callback=parsing.ParameterList(parsing.supported_attacks))
@click.argument('evasion_attacks',
callback=parsing.ParameterList(parsing.supported_attacks))
@click.argument('p',
type=click.Choice(parsing.distances),
callback=parsing.validate_lp_distance)
@click.argument('rejection_threshold', type=float)
@click.argument('substitute_state_dict_paths',
callback=parsing.ParameterList())
@click.option(
'--state-dict-path',
type=click.Path(exists=True, file_okay=True, dir_okay=False),
default=None,
help=
'The path to the state-dict file of the model. If None, a pretrained model will be used (if available).'
)
import detectors
import parsing
import tests
import utils
logger = logging.getLogger(__name__)
# Nota: In questo test, il rejection_threshold indica "se togli l'attacco corrispondente, quanto deve ottenere la detector pool per rifiutare?"
@click.command()
@click.argument('domain', type=click.Choice(parsing.domains))
@click.argument('architecture', type=click.Choice(parsing.architectures))
@click.argument('dataset')
@click.argument('attacks',
callback=parsing.ParameterList(parsing.supported_attacks))
@click.argument('p',
type=click.Choice(parsing.distances),
callback=parsing.validate_lp_distance)
@click.argument('rejection_thresholds',
callback=parsing.ParameterList(cast_to=float))
@click.argument('substitute_state_dict_paths',
callback=parsing.ParameterList())
@click.option(
'--state-dict-path',
type=click.Path(exists=True, file_okay=True, dir_okay=False),
default=None,
help=
'The path to the state-dict file of the model. If None, a pretrained model will be used (if available).'
)
@click.option(
import click
import torch
import parsing
import tests
import utils
logger = logging.getLogger(__name__)
@click.command()
@click.argument('domain', type=click.Choice(parsing.domains))
@click.argument('architecture', type=click.Choice(parsing.architectures))
@click.argument('dataset')
@click.argument('attacks', callback=parsing.ParameterList(parsing.supported_attacks))
@click.argument('p', type=click.Choice(parsing.distances), callback=parsing.validate_lp_distance)
@click.option('--state-dict-path', type=click.Path(exists=True, file_okay=True, dir_okay=False), default=None,
help='The path to the state-dict file of the model. If None, a pretrained model will be used (if available).')
@click.option('--masked-relu', is_flag=True,
help='If passed, all ReLU layers will be converted to MaskedReLU layers.')
@click.option('--batch-size', type=click.IntRange(1, None), default=50, show_default=True,
help='The batch size of the dataset.')
@click.option('--device', default='cuda', show_default=True, help='The device where the model will be executed.')
@click.option('--cpu-threads', type=click.IntRange(1, None, False), default=None,
help='The number of PyTorch CPU threads. If unspecified, the default '
'number is used (usually the number of cores).')
@click.option('--attack-config-file', type=click.Path(exists=True, file_okay=True, dir_okay=False),
default='default_attack_configuration.cfg', show_default=True, help='The path to the file containing the '
'attack configuration.')
@click.option('--misclassification-policy', type=click.Choice(parsing.misclassification_policies),
import detectors
import parsing
import tests
import utils
logger = logging.getLogger(__name__)
# TODO: La CLI è scomoda quando devi passare valori negativi
@click.command()
@click.argument('domain', type=click.Choice(parsing.domains))
@click.argument('architecture', type=click.Choice(parsing.architectures))
@click.argument('dataset')
@click.argument('attacks', callback=parsing.ParameterList(parsing.supported_attacks))
@click.argument('p', type=click.Choice(parsing.distances), callback=parsing.validate_lp_distance)
@click.argument('rejection_thresholds', callback=parsing.ParameterList(cast_to=float))
@click.argument('substitute_state_dict_paths', callback=parsing.ParameterList())
@click.option('--state-dict-path', type=click.Path(exists=True, file_okay=True, dir_okay=False), default=None,
help='The path to the state-dict file of the model. If None, a pretrained model will be used (if available).')
@click.option('--masked-relu', is_flag=True,
help='If passed, all ReLU layers will be converted to MaskedReLU layers.')
@click.option('--batch-size', type=click.IntRange(1, None), default=50, show_default=True,
help='The batch size of the dataset.')
@click.option('--device', default='cuda', show_default=True, help='The device where the model will be executed.')
@click.option('--cpu-threads', type=click.IntRange(1, None, False), default=None,
help='The number of PyTorch CPU threads. If unspecified, the default '
'number is used (usually the number of cores).')
@click.option('--attack-config-file', type=click.Path(exists=True, file_okay=True, dir_okay=False),
default='default_attack_configuration.cfg', show_default=True, help='The path to the file containing the '
all_success_rates.append(success_rate)
all_differences_between_averages.append(
difference_between_averages)
all_r2s.append(r_value**2)
return (np.mean(all_success_rates), np.std(all_success_rates)), (
np.mean(all_differences_between_averages),
np.std(all_differences_between_averages)), (np.mean(all_r2s),
np.std(all_r2s))
def pool_selector(pool):
(success_mean, _), (_, _), (r2_mean, _) = pool
return (success_mean, r2_mean)
@click.command()
@click.argument('domain')
@click.argument('parameter_set')
@click.argument('atol', type=float)
@click.argument('rtol', type=float)
@click.argument('attacks', callback=parsing.ParameterList())
@click.option('--test-override', type=str, default=None)
def main(domain, parameter_set, atol, rtol, attacks, test_override):
pool_result = get_r2(domain, parameter_set, atol, rtol, test_override,
attacks)
print(pool_result)
if __name__ == '__main__':
main()
@click.option('--rotation',
type=float,
default=0,
show_default=True,
help='Random rotation (in degrees) in range (-value, +value).')
@click.option(
'--translation',
type=float,
default=0,
show_default=True,
help=
'Random horizontal and vertical translation in range (-value * image_size, +value * image_size).'
)
@click.option(
'--adversarial-training',
callback=parsing.ParameterList(parsing.epsilon_attacks),
default=None,
help='The adversarial attack that will be used to compute the adversarials. '
'If unspecified, disables adversarial training. Requires specifying --adversarial-ratio, --adversarial-p '
'and --adversarial-eps.')
@click.option('--adversarial-ratio',
type=float,
default=None,
help='The ratio of samples that are replaced with adversarials. '
'Ignored if adversarial training is disabled. '
'Required if adversarial training is enabled.')
@click.option('--adversarial-p',
type=click.Choice(parsing.distances),
callback=parsing.validate_lp_distance,
default=None,
help='The Lp norm used for adversarial training. '