def add_argument(
cls, group: argparse._ArgumentGroup) -> argparse._ArgumentGroup:
super().add_argument(group)
group.add_argument('--adv_train',
dest='adv_train',
action='store_true',
help='enable adversarial training.')
group.add_argument(
'--adv_train_alpha',
dest='adv_train_alpha',
type=float,
help='adversarial training PGD alpha, defaults to 2/255.')
group.add_argument(
'--adv_train_epsilon',
dest='adv_train_epsilon',
type=float,
help='adversarial training PGD epsilon, defaults to 8/255.')
group.add_argument(
'--adv_train_valid_epsilon',
dest='adv_train_valid_epsilon',
type=float,
help='adversarial training PGD epsilon, defaults to 8/255.')
group.add_argument(
'--adv_train_iter',
dest='adv_train_iter',
type=int,
help='adversarial training PGD iteration, defaults to 7.')
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument(
'--original',
dest='original',
action='store_true',
help='load original clean model, defaults to False.')
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument('--cutout', action='store_true', help='use cutout')
group.add_argument('--cutout_length',
type=int,
default=16,
help='cutout length')
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument('--tabor_hyperparams',
type=list,
help='list of weights for regularization terms '
'(default: [1e-6, 1e-5, 1e-7, 1e-8, 0, 1e-2])')
return group
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument('--poison_percent', type=float,
help='malicious training data injection probability for each batch, defaults to 0.01')
group.add_argument('--train_mode', choices=['batch', 'dataset'],
help='target class of backdoor, defaults to \'batch\'')
return group
def add_encoder_general_arguments(group: _ArgumentGroup) -> _ArgumentGroup:
"""Define general arguments for encoder."""
group.add_argument(
"--etype",
default="blstmp",
type=str,
choices=[
"custom",
"lstm",
"blstm",
"lstmp",
"blstmp",
"vgglstmp",
"vggblstmp",
"vgglstm",
"vggblstm",
"gru",
"bgru",
"grup",
"bgrup",
"vgggrup",
"vggbgrup",
"vgggru",
"vggbgru",
],
help="Type of encoder network architecture",
)
group.add_argument(
"--dropout-rate",
default=0.0,
type=float,
help="Dropout rate for the encoder",
)
return group
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument('--attack_source',
help='attack source (default: "badnet")')
group.add_argument('--mark_source',
help='mark source (default: "attack")')
return group
def _add_argument(cls, ap: argparse._ArgumentGroup, field: Field) -> None:
assert cls._is_argument_field(field)
assert not isinstance(
field.type, ForwardRef), "rework class to not have forward ref"
target_type = field.type
if hasattr(field.type, "__args__"):
target_type = field.type.__args__[0]
argparse_type = target_type
metavar = target_type.__name__
if issubclass(target_type, Enum):
argparse_type = common.argparse_choices_pre_type(
[m.name for m in target_type],
lambda s: target_type[s],
)
metavar = f"[{','.join(m.name for m in target_type)}]"
help = "[missing] Add a help annotation on the config class!"
if field.metadata:
metavar = field.metadata.get("metavar", metavar)
help = field.metadata.get("help", help)
ap.add_argument(
f"--{field.name.replace('_', '-')}",
type=argparse_type,
metavar=metavar,
required=field.default is MISSING
and field.default_factory is MISSING, # type: ignore
help=help,
)
def registerArgparse(self, group: ArgumentGroup):
enables = [x for x in self.options if x[0] != "!"]
disables = [x[1:] for x in self.options if x[0] == "!"]
group.add_argument(*enables, dest=self.name, help=self.description + formatDefault(self.value), action='store_true', default=None)
if disables:
group.add_argument(*disables, dest=self.name, help=self.description + formatDefault(self.value), action='store_false', default=None)
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument('--original',
action='store_true',
help='whether to load original clean model '
'(default: False)')
return group
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument(
'--prune_ratio',
type=float,
help='the ratio of neuron number to prune (default: 0.95)')
return group
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument(
'--preprocess_layer',
help=
'the chosen layer used to extract feature representation, defaults to ``flatten``'
)
group.add_argument(
'--poison_image_num',
type=int,
help=
'the number of sampled poison image to train the model initially, defaults to 50'
)
group.add_argument(
'--clean_image_num',
type=int,
help=
'the number of sampled clean image to train the model initially, defaults to 500'
)
group.add_argument(
'--epsilon',
type=int,
help=
'the number of examples to remove from each class, defaults to 5')
group.add_argument(
'--retrain_epoch',
type=int,
help=
'the epoch to retrain the model on clean image dataset, defaults to 5'
)
return group
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument('--attack_source',
help='attack source, defaults to ``badnet``')
group.add_argument('--mark_source',
help='mark source, defaults to ``attack``')
return group
def _add_custom_limits(parser: argparse._ArgumentGroup) -> None:
def minmax_type(string: str) -> float:
value = float(string)
if value < 0 or value > 8:
raise argparse.ArgumentTypeError(
f'{string} must be between 0.0 and 8.0', )
return value
high_entropy_help_text = (
'Sets the entropy limit for high entropy strings. '
'Value must be between 0.0 and 8.0,')
# NOTE: This doesn't have explicit default values since we want to be able to determine
# the difference between a value set by default, and an explicitly set value (which happens
# to be the same as the default value). This distinction plays an important role when doing
# precedence calculation (default value < baseline config < CLI explicit value)
parser.add_argument(
'--base64-limit',
type=minmax_type,
nargs='?',
help=high_entropy_help_text + ' defaults to 4.5.',
)
parser.add_argument(
'--hex-limit',
type=minmax_type,
nargs='?',
help=high_entropy_help_text + ' defaults to 3.0.',
)
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument(
'--class_sample_num',
type=int,
help=
'the number of sampled images per class, defaults to config[latent_backdoor][class_sample_num][dataset]=100'
)
group.add_argument(
'--mse_weight',
type=float,
help=
'the weight of mse loss during retraining, defaults to config[latent_backdoor][mse_weight][dataset]=100'
)
group.add_argument(
'--preprocess_layer',
help=
'the chosen feature layer patched by trigger, defaults to \'features\''
)
group.add_argument('--preprocess_epoch',
type=int,
help='preprocess optimization epoch')
group.add_argument('--preprocess_lr',
type=float,
help='preprocess learning rate')
return group
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument('--defense_input_num',
type=int,
help='the number of training inputs to evaluate '
'(default: None)')
return group
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument(
'--data_format',
dest='data_format',
type=str,
help='folder, zip or numpy. (zip is using ZIP_STOREED)')
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument('--seed_num',
dest='seed_num',
type=int,
help='ABS seed number, defaults to -5.')
group.add_argument(
'--max_troj_size',
dest='max_troj_size',
type=int,
help='ABS max trojan trigger size (pixel number), defaults to 64.')
group.add_argument('--remask_epoch',
dest='remask_epoch',
type=int,
help='ABS optimizing epoch, defaults to 1000.')
group.add_argument(
'--remask_lr',
dest='remask_lr',
type=float,
help='ABS optimization learning rate, defaults to 0.1.')
group.add_argument(
'--remask_weight',
dest='remask_weight',
type=float,
help='ABS optimization remask loss weight, defaults to 0.1.')
def consumer_set_id_arg(req_args: _ArgumentGroup,
opt_args: _ArgumentGroup) -> None:
req_args.add_argument(
"--consumer-set-id",
type=str,
help="The identifier of the consumer set",
required=True,
)
def env_arg(opt_args: _ArgumentGroup) -> None:
opt_args.add_argument(
"--env",
choices=["prod", "dev", "local"],
default=None,
type=str,
help="Switches auth context.",
)
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument('--model_index', type=int, required=True)
group.add_argument('--model_seed', type=int)
group.add_argument('--nats_path')
group.add_argument('--autodl_path')
group.add_argument('--search_space')
return group
def output_fmt_arg(req_args: _ArgumentGroup, opt_args: _ArgumentGroup) -> None:
opt_args.add_argument(
"-o",
"--output-fmt",
choices=["table", "json"],
type=str,
help="Switches output format.",
)
def _add_disable_flag(parser: argparse._ArgumentGroup) -> None:
parser.add_argument(
'--disable-filter',
type=str,
nargs=1,
action='append', # so we can support multiple flags with same value
help=
'Specify filter to disable. e.g. detect_secrets.filters.common.is_invalid_file',
)
def org_plan_type_arg(req_args: _ArgumentGroup,
opt_args: _ArgumentGroup) -> None:
req_args.add_argument(
"--plan-type",
choices=[1, 2, 3, 4, 5, 6],
type=int,
help="Plan type for organization.",
required=True,
)
def no_org_arg(req_args: _ArgumentGroup, opt_args: _ArgumentGroup) -> None:
opt_args.add_argument(
"--no-org",
nargs="?",
default=False,
const=True,
type=bool,
help="Only show users that are not part of any organization.",
)
def args_addr(argument_group: argparse._ArgumentGroup) -> None: # pylint: disable=protected-access
"""Create addr positional argumenmt."""
argument_group.add_argument(
"addr",
type=str,
help="""Address to listen or connect to.
""",
)
def configure_parser(cls, arg_group: _ArgumentGroup) -> None:
arg_group.add_argument(
'--force-beam-block-number',
type=int,
help="Force beam sync to activate on a specific block number (for testing)",
default=None,
)
add_disable_backfill_arg(arg_group)
add_sync_from_checkpoint_arg(arg_group)
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument(
'--prune_ratio',
dest='prune_ratio',
type=float,
help=
'the ratio of neuron number to prune, defaults to config[fine_pruning][prune_ratio]=0.95'
)
def args_quick(argument_group: argparse._ArgumentGroup) -> None: # pylint: disable=protected-access
"""Create -q/--quick argumenmt."""
argument_group.add_argument(
"-q",
"--quick",
action="store_true",
help="""Show quick payload results (less detail).
""",
)
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument(
'--data_format',
choices=['folder', 'tar', 'zip'],
help='file format of dataset. (zip is using ZIP_STORED)')
group.add_argument('--memory',
action='store_true',
help='put all dataset into memory initialization.')
return group
