def add_auxiliary_task_arguments(group: _ArgumentGroup) -> _ArgumentGroup:
"""Add arguments for auxiliary task."""
group.add_argument(
"--use-ctc-loss",
type=strtobool,
nargs="?",
default=False,
help="Whether to compute auxiliary CTC loss.",
)
group.add_argument(
"--ctc-loss-weight",
default=0.5,
type=float,
help="Weight of auxiliary CTC loss.",
)
group.add_argument(
"--ctc-loss-dropout-rate",
default=0.0,
type=float,
help="Dropout rate for auxiliary CTC.",
)
group.add_argument(
"--use-lm-loss",
type=strtobool,
nargs="?",
default=False,
help="Whether to compute auxiliary LM loss (label smoothing).",
)
group.add_argument(
"--lm-loss-weight",
default=0.5,
type=float,
help="Weight of auxiliary LM loss.",
)
group.add_argument(
"--lm-loss-smoothing-rate",
default=0.0,
type=float,
help="Smoothing rate for LM loss. If > 0, label smoothing is enabled.",
)
group.add_argument(
"--use-aux-transducer-loss",
type=strtobool,
nargs="?",
default=False,
help="Whether to compute auxiliary Transducer loss.",
)
group.add_argument(
"--aux-transducer-loss-weight",
default=0.2,
type=float,
help="Weight of auxiliary Transducer loss.",
)
group.add_argument(
"--aux-transducer-loss-enc-output-layers",
default=None,
type=ast.literal_eval,
help="List of intermediate encoder layers for auxiliary "
"transducer loss computation.",
)
group.add_argument(
"--aux-transducer-loss-mlp-dim",
default=320,
type=int,
help=
"Multilayer perceptron hidden dimension for auxiliary Transducer loss.",
)
group.add_argument(
"--aux-transducer-loss-mlp-dropout-rate",
default=0.0,
type=float,
help=
"Multilayer perceptron dropout rate for auxiliary Transducer loss.",
)
group.add_argument(
"--use-symm-kl-div-loss",
type=strtobool,
nargs="?",
default=False,
help="Whether to compute symmetric KL divergence loss.",
)
group.add_argument(
"--symm-kl-div-loss-weight",
default=0.2,
type=float,
help="Weight of symmetric KL divergence loss.",
)
return group
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument('--pgd_alpha', dest='pgd_alpha', type=float)
group.add_argument('--pgd_epsilon', dest='pgd_epsilon', type=float)
group.add_argument('--pgd_iteration', dest='pgd_iteration', type=int)
group.add_argument('--stop_conf', dest='stop_conf', type=float)
group.add_argument('--magnet', dest='magnet', action='store_true')
group.add_argument('--randomized_smooth',
dest='randomized_smooth',
action='store_true')
group.add_argument('--curvature',
dest='curvature',
action='store_true')
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument('--transform_mode', dest='transform_mode', type=str,
help='Image Transform Mode, defaults to "recompress".')
group.add_argument('--resize_ratio', dest='resize_ratio', type=float,
help='Image Resize Ratio for Recompress, defaults to 0.95.')
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument('--term')
group.add_argument('--inner_iter', type=int)
group.add_argument('--inner_lr', type=float)
group.add_argument(
'--class_sample_num',
type=int,
help='the number of sampled images per class, defaults to 100')
group.add_argument(
'--mse_weight',
type=float,
help='the weight of mse loss during retraining, defaults to 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('--sample_ratio',
type=float,
help='sample ratio from the full training data')
group.add_argument('--noise_dim', type=int, help='GAN noise dimension')
group.add_argument('--remask_epoch',
type=int,
help='Remask optimizing epoch')
group.add_argument('--remask_lr',
type=float,
help='Remask optimizing learning rate')
group.add_argument('--gamma_1',
type=float,
help='control effect of GAN loss')
group.add_argument('--gamma_2',
type=float,
help='control effect of perturbation loss')
return group
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument('--target_platform')
return group
def add_argument(group: argparse._ArgumentGroup):
group.add_argument(
'--edge_color',
dest='edge_color',
help='edge color in watermark image, defaults to \'auto\'.')
group.add_argument(
'--mark_path',
dest='mark_path',
help=
'edge color in watermark image, defaults to trojanzoo/data/mark/apple_white.png.'
)
group.add_argument('--mark_alpha',
dest='mark_alpha',
type=float,
help='mark transparency, defaults to 0.0.')
group.add_argument('--mark_height',
dest='mark_height',
type=int,
help='mark height, defaults to 3.')
group.add_argument('--mark_width',
dest='mark_width',
type=int,
help='mark width, defaults to 3.')
group.add_argument('--height_offset',
dest='height_offset',
type=int,
help='height offset, defaults to 0')
group.add_argument('--width_offset',
dest='width_offset',
type=int,
help='width offset, defaults to 0')
group.add_argument('--random_pos',
dest='random_pos',
action='store_true',
help='Random offset Location for add_mark.')
group.add_argument('--random_init',
dest='random_init',
action='store_true',
help='random values for mark pixel.')
group.add_argument('--mark_distributed',
dest='mark_distributed',
action='store_true',
help='Distributed Mark.')
return group
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument('--select_point',
dest='select_point',
type=int,
help='the number of select_point, defaults to 2')
def add_argument(
cls, group: argparse._ArgumentGroup) -> argparse._ArgumentGroup:
r"""Add image dataset arguments to argument parser group.
View source to see specific arguments.
Note:
This is the implementation of adding arguments.
The concrete dataset class may override this method to add more arguments.
For users, please use :func:`add_argument()` instead, which is more user-friendly.
See Also:
:meth:`trojanzoo.datasets.Dataset.add_argument()`
"""
super().add_argument(group)
group.add_argument(
'--dataset_normalize',
dest='normalize',
action='store_true',
help='use transforms.Normalize in dataset transform. '
'(It\'s used in model as the first layer by default.)')
group.add_argument('--transform',
choices=[None, 'none', 'bit', 'pytorch'])
group.add_argument('--auto_augment',
action='store_true',
help='use auto augment')
group.add_argument('--mixup', action='store_true', help='use mixup')
group.add_argument('--mixup_alpha',
type=float,
help='mixup alpha (default: 0.0)')
group.add_argument('--cutmix', action='store_true', help='use cutmix')
group.add_argument('--cutmix_alpha',
type=float,
help='cutmix alpha (default: 0.0)')
group.add_argument('--cutout', action='store_true', help='use cutout')
group.add_argument('--cutout_length', type=int, help='cutout length')
return group
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument('--mix_image_num', type=int, help='the number of sampled image')
group.add_argument('--clean_image_ratio', type=float, help='the ratio of clean image')
group.add_argument('--retrain_epoch', type=int, help='the epoch of retraining the model')
group.add_argument('--nb_clusters', type=int, help='')
group.add_argument('--clustering_method', type=str, help='the amount of clusters')
group.add_argument('--nb_dims', type=int,
help='the dimension set in the process of reduceing the dimensionality of data')
group.add_argument('--reduce_method', type=str, help=' the method for reduing the dimensionality of data')
group.add_argument('--cluster_analysis', type=str, help='the method chosen to analyze whether cluster is the poison cluster, '
'including size, distance, relative-size, silhouette-scores')
return group
def add_argument(group: argparse._ArgumentGroup):
group.add_argument('-m', '--model', dest='model_name',
help='model name, defaults to config[model][default_model]')
group.add_argument('--suffix', dest='suffix',
help='model name suffix, e.g. _adv_train')
group.add_argument('--pretrain', dest='pretrain', action='store_true',
help='load pretrained weights, defaults to False')
group.add_argument('--official', dest='official', action='store_true',
help='load official weights, defaults to False')
group.add_argument('--model_dir', dest='model_dir',
help='directory to contain pretrained models')
group.add_argument('--randomized_smooth', dest='randomized_smooth', action='store_true',
help='whether to use randomized smoothing, defaults to False')
group.add_argument('--rs_sigma', dest='rs_sigma', type=float,
help='randomized smoothing sampling std, defaults to 0.01')
group.add_argument('--rs_n', dest='rs_n', type=int,
help='randomized smoothing sampling number, defaults to 100')
return group
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument('--num_classes', type=int, help='number of classes')
return group
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument(
'--alpha',
dest='alpha',
type=float,
help='PGD learning rate per step, defaults to 3.0/255')
group.add_argument(
'--epsilon',
dest='epsilon',
type=float,
help='Projection norm constraint, defaults to 8.0/255')
group.add_argument('--iteration',
dest='iteration',
type=int,
help='Attack Iteration, defaults to 20')
group.add_argument('--stop_threshold',
dest='stop_threshold',
type=float,
help='early stop confidence, defaults to None')
group.add_argument(
'--target_idx',
dest='target_idx',
type=int,
help='Target label order in original classification, defaults to 1 '
'(0 for untargeted attack, 1 for most possible class, -1 for most unpossible class)'
)
group.add_argument(
'--grad_method',
dest='grad_method',
help='gradient estimation method, defaults to \'white\'')
group.add_argument(
'--query_num',
dest='query_num',
type=int,
help=
'query numbers for black box gradient estimation, defaults to 100.'
)
group.add_argument(
'--sigma',
dest='sigma',
type=float,
help=
'gaussian sampling std for black box gradient estimation, defaults to 1e-3'
)
def add_custom_encoder_arguments(group: _ArgumentGroup) -> _ArgumentGroup:
"""Define arguments for Custom encoder."""
group.add_argument(
"--enc-block-arch",
type=eval,
action="append",
default=None,
help="Encoder architecture definition by blocks",
)
group.add_argument(
"--enc-block-repeat",
default=1,
type=int,
help="Repeat N times the provided encoder blocks if N > 1",
)
group.add_argument(
"--custom-enc-input-layer",
type=str,
default="conv2d",
choices=["conv2d", "vgg2l", "linear", "embed"],
help="Custom encoder input layer type",
)
group.add_argument(
"--custom-enc-input-dropout-rate",
type=float,
default=0.0,
help="Dropout rate of custom encoder input layer",
)
group.add_argument(
"--custom-enc-input-pos-enc-dropout-rate",
type=float,
default=0.0,
help=
"Dropout rate of positional encoding in custom encoder input layer",
)
group.add_argument(
"--custom-enc-positional-encoding-type",
type=str,
default="abs_pos",
choices=["abs_pos", "scaled_abs_pos", "rel_pos"],
help="Custom encoder positional encoding layer type",
)
group.add_argument(
"--custom-enc-self-attn-type",
type=str,
default="self_attn",
choices=["self_attn", "rel_self_attn"],
help="Custom encoder self-attention type",
)
group.add_argument(
"--custom-enc-pw-activation-type",
type=str,
default="relu",
choices=["relu", "hardtanh", "selu", "swish"],
help="Custom encoder pointwise activation type",
)
group.add_argument(
"--custom-enc-conv-mod-activation-type",
type=str,
default="swish",
choices=["relu", "hardtanh", "selu", "swish"],
help="Custom encoder convolutional module activation type",
)
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 or zip. (zip is using ZIP_STORED)')
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument('--adv_train',
action='store_true',
help='enable adversarial training.')
group.add_argument(
'--adv_train_iter',
type=int,
help='adversarial training PGD iteration, defaults to 7.')
group.add_argument(
'--adv_train_alpha',
type=float,
help='adversarial training PGD alpha, defaults to 2/255.')
group.add_argument(
'--adv_train_eps',
type=float,
help='adversarial training PGD eps, defaults to 8/255.')
group.add_argument(
'--adv_train_valid_eps',
type=float,
help='adversarial training PGD eps, defaults to 8/255.')
group.add_argument(
'--sgm',
action='store_true',
help='whether to use sgm gradient, defaults to False')
group.add_argument('--sgm_gamma',
type=float,
help='sgm gamma, defaults to 1.0')
return group
def add_argument(cls, group: argparse._ArgumentGroup):
r"""Add image model arguments to argument parser group.
View source to see specific arguments.
Note:
This is the implementation of adding arguments.
The concrete model class may override this method to add more arguments.
For users, please use :func:`add_argument()` instead, which is more user-friendly.
See Also:
:meth:`trojanzoo.models.Model.add_argument()`
"""
super().add_argument(group)
group.add_argument('--adv_train', choices=[None, 'pgd', 'free', 'trades'],
help='adversarial training (default: None)')
group.add_argument('--adv_train_random_init', action='store_true')
group.add_argument('--adv_train_iter', type=int,
help='adversarial training PGD iteration (default: 7).')
group.add_argument('--adv_train_alpha', type=float,
help='adversarial training PGD alpha (default: 2/255).')
group.add_argument('--adv_train_eps', type=float,
help='adversarial training PGD eps (default: 8/255).')
group.add_argument('--adv_train_eval_iter', type=int)
group.add_argument('--adv_train_eval_alpha', type=float)
group.add_argument('--adv_train_eval_eps', type=float)
group.add_argument('--adv_train_trades_beta', type=float,
help='regularization, i.e., 1/lambda in TRADES '
'(default: 6.0)')
group.add_argument('--norm_layer', choices=['bn', 'gn'], default='bn')
group.add_argument('--sgm', action='store_true',
help='whether to use sgm gradient (default: False)')
group.add_argument('--sgm_gamma', type=float,
help='sgm gamma (default: 1.0)')
return group
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument('--preprocess_layer', dest='preprocess_layer', type=str,
help='the chosen feature layer patched by trigger where rare neuron activation is maxmized, defaults to ``flatten``')
group.add_argument('--threshold', dest='threshold', type=float,
help='Trojan Net Threshold, defaults to 5')
group.add_argument('--target_value', dest='target_value', type=float,
help='Trojan Net Target_Value, defaults to 10')
group.add_argument('--neuron_lr', dest='neuron_lr', type=float,
help='Trojan Net learning rate in neuron preprocessing, defaults to 0.015')
group.add_argument('--neuron_epoch', dest='neuron_epoch', type=int,
help='Trojan Net epoch in neuron preprocessing, defaults to 20')
group.add_argument('--neuron_num', dest='neuron_num', type=int,
help='Trojan Net neuron numbers in neuron preprocessing, defaults to 2')
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument(
'--poison_generation_method',
dest='poison_generation_method',
type=str,
help=
'the chosen method to generate poisoned sample, defaults to config[clean_label][poison_generation_method]=pgd'
)
group.add_argument(
'--tau',
dest='tau',
type=float,
help=
'the interpolation constant used to balance source imgs and target imgs, defaults to config[clean_label][tau]=0.2'
)
group.add_argument(
'--epsilon',
dest='epsilon',
type=float,
help=
'the perturbation bound in input space, defaults to config[clean_label][epsilon]=0.1, 300/(3*32*32)'
)
group.add_argument(
'--noise_dim',
dest='noise_dim',
type=int,
help=
'the dimension of the input in the generator, defaults to config[clean_label][noise_dim]=100'
)
group.add_argument(
'--train_gan',
dest='train_gan',
action='store_true',
help='whether train the GAN if it already exists, defaults to False'
)
group.add_argument(
'--generator_iters',
dest='generator_iters',
type=int,
help=
' the epoch for training the generator, defaults to config[clean_label][generator_iters]=1000'
)
group.add_argument(
'--critic_iter',
dest='critic_iter',
type=int,
help=
' the critic iterations per generator training iteration, defaults to config[clean_label][critic_iter]=5'
)
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument('--poison_generation_method', choices=['pgd', 'gan'],
help='the chosen method to generate poisoned sample '
'(default: "pgd")')
group.add_argument('--pgd_alpha', type=float)
group.add_argument('--pgd_eps', type=float)
group.add_argument('--pgd_iter', type=int)
group.add_argument('--tau', type=float,
help='the interpolation constant used to balance source imgs and target imgs, '
'defaults to 0.2')
group.add_argument('--noise_dim', type=int,
help='the dimension of the input in the generator, '
'defaults to config[clean_label][noise_dim]=100')
group.add_argument('--train_gan', action='store_true',
help='whether train the GAN if it already exists, defaults to False')
group.add_argument('--generator_iters', type=int,
help='epochs for training the generator, defaults to 1000')
group.add_argument('--critic_iter', type=int,
help='critic iterations per generator training iteration '
'(default: 5)')
return group
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument('--supernet',
action='store_true',
help='whether to use supernet')
group.add_argument('--model_arch',
help='genotype name (default: "darts")')
group.add_argument('--layers',
type=int,
help='total number of layers (default: 20)')
group.add_argument('--init_channels',
type=int,
help='out_channel of stem conv layer (default: 36)')
group.add_argument('--dropout_p',
type=float,
help='dropout probability (default: 0.2)')
group.add_argument('--auxiliary',
action='store_true',
help='whether to use auxiliary classifier')
group.add_argument(
'--auxiliary_weight',
type=float,
help='loss weight of auxiliary classifier (default: 0.4)')
group.add_argument(
'--arch_search',
action='store_true',
help='whether to search supernet architecture weight parameters')
group.add_argument(
'--use_full_train_set',
action='store_true',
help='whether to use full training data during architecture search'
)
group.add_argument(
'--arch_lr',
type=float,
help='learning rate for architecture optimizer (default: 3e-4)')
group.add_argument(
'--arch_weight_decay',
type=float,
help='weight decay for architecture optimizer (default: 1e-3)')
group.add_argument(
'--arch_unrolled',
action='store_true',
default=False,
help='whether to use one-step unrolled validation loss (darts-v2)')
return group
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument('--abs_weight', dest='abs_weight', type=float)
group.add_argument('--strip_percent', dest='strip_percent', type=float)
def add_argument(group: argparse._ArgumentGroup):
group.add_argument('-d',
'--dataset',
dest='dataset_name',
type=str,
help='dataset name (lowercase).')
group.add_argument(
'--batch_size',
dest='batch_size',
type=int,
help='batch size (negative number means batch_size for each gpu).')
group.add_argument('--valid_batch_size',
dest='valid_batch_size',
type=int,
help='valid batch size.')
group.add_argument('--test_batch_size',
dest='test_batch_size',
type=int,
help='test batch size.')
group.add_argument(
'--num_workers',
dest='num_workers',
type=int,
help=
'num_workers passed to torch.utils.data.DataLoader for training set, defaults to 4.'
)
group.add_argument(
'--download',
dest='download',
action='store_true',
help='download dataset if not exist by calling dataset.initialize()'
)
group.add_argument('--data_dir',
dest='data_dir',
help='directory to contain datasets')
return group
def add_args_to_group(cls, group: ArgumentGroup):
group.add_argument("--hidden-default-argument",
dest="hidden_default_argument",
default=3)
group.add_argument("--hidden-argument", dest="hidden_argument")
def add_to_group(self, group: argparse._ArgumentGroup) -> None:
group.add_argument(*self.args, **self.kwargs)
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument(
'--pgd_alpha',
type=float,
help='PGD learning rate per step, defaults to 2.0/255')
group.add_argument(
'--pgd_eps',
type=float,
help='Projection norm constraint, defaults to 8.0/255')
group.add_argument('--iteration',
type=int,
help='Attack Iteration, defaults to 7')
group.add_argument('--stop_threshold',
type=float,
help='early stop confidence, defaults to 0.99')
group.add_argument(
'--target_idx',
type=int,
help='Target label order in original classification, defaults to -1 '
'(0 for untargeted attack, 1 for most possible class, -1 for most unpossible class)'
)
group.add_argument(
'--test_num',
type=int,
help='total number of test examples for PGD, defaults to 1000.')
group.add_argument(
'--num_init',
type=int,
help=
'number of random init for PGD, defaults to 0 (without random initialization).'
)
group.add_argument(
'--grad_method',
help='gradient estimation method, defaults to \'white\'')
group.add_argument(
'--query_num',
type=int,
help=
'query numbers for black box gradient estimation, defaults to 100.'
)
group.add_argument(
'--sigma',
type=float,
help=
'gaussian sampling std for black box gradient estimation, defaults to 1e-3'
)
return group
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument('--attack_adapt', dest='attack_adapt', action='store_true',
help='Adaptive attack to add fake queries.')
group.add_argument('--fake_percent', dest='fake_percent', type=float,
help='fake query percentage.')
group.add_argument('--dist', dest='dist', type=float,
help='fake query noise std.')
group.add_argument('--defend_adapt', dest='defend_adapt', action='store_true',
help='Robust location M-estimator.')
group.add_argument('--active', dest='active', action='store_true',
help='Proactive solicitation.')
group.add_argument('--active_percent', dest='active_percent', type=float,
help='Active gradient weight.')
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument('--pgd_alpha', type=float)
group.add_argument('--pgd_eps', type=float)
group.add_argument('--pgd_iter', type=int)
return group
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument('--inner_iter', dest='inner_iter', type=int)
group.add_argument('--inner_lr', dest='inner_lr', type=float)
def add_custom_training_arguments(group: _ArgumentGroup) -> _ArgumentGroup:
"""Define arguments for training with Custom architecture."""
group.add_argument(
"--optimizer-warmup-steps",
default=25000,
type=int,
help="Optimizer warmup steps",
)
group.add_argument(
"--noam-lr",
default=10.0,
type=float,
help="Initial value of learning rate",
)
group.add_argument(
"--noam-adim",
default=0,
type=int,
help="Most dominant attention dimension for scheduler.",
)
group.add_argument(
"--transformer-warmup-steps",
type=int,
help="Optimizer warmup steps. The parameter is deprecated, "
"please use --optimizer-warmup-steps instead.",
dest="optimizer_warmup_steps",
)
group.add_argument(
"--transformer-lr",
type=float,
help="Initial value of learning rate. The parameter is deprecated, "
"please use --noam-lr instead.",
dest="noam_lr",
)
group.add_argument(
"--adim",
type=int,
help="Most dominant attention dimension for scheduler. "
"The parameter is deprecated, please use --noam-adim instead.",
dest="noam_adim",
)
return group