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Functions327 in github.com/SCLBD/MCG-Blackbox

⨍ Functions 327◇ Types & classes 75
↓ 1 callersFunctionis_url
Determine whether the given object is a valid URL string.
utils/commons.py:380
↓ 1 callersFunctionlatent_initialize
Note: Normal latent_base shape (1, 48, 4, 4) Normal latent_vec shape [(1, 48, 4, 4), (1, 6, 16, 16), (1, 12, 8, 8)] Corre
models/flow_latent.py:32
↓ 1 callersMethodlifelong_forward_loss
(self, surrogate_model, optimizer, history_batch, current_batch)
utils/surrogate_trainer.py:31
↓ 1 callersMethodlikelihood
(mean, logs, x)
models/modules.py:384
↓ 1 callersFunctionload_generator
(args)
utils/load_models.py:21
↓ 1 callersFunctionload_imagenet_model
(model_name, require_optim=False, defence_method=None)
utils/load_models.py:59
↓ 1 callersMethodload_params
(self, key, value, args)
attacks/ifgsm_attack.py:72
↓ 1 callersFunctionloss_function
(x, label, targeted)
attacks/base_attack.py:7
↓ 1 callersMethodmake_batch
(self)
utils/buffer.py:149
↓ 1 callersFunctionmake_cache_dir_path
(*paths: str)
utils/commons.py:126
↓ 1 callersFunctionmargin_loss_interface
(model, class_num)
attacks/base_attack.py:4
↓ 1 callersFunctionmeta_finetune
(generator, adv_models, images, labels, latent, args, meta_iteration=1)
utils/finetune.py:94
↓ 1 callersMethodmeta_test
(self)
trainners/MetaLearner.py:211
↓ 1 callersMethodmeta_train
Reptile meta training process
trainners/MetaLearner.py:167
↓ 1 callersMethodnew
(self)
utils/buffer.py:23
↓ 1 callersFunctionnormalize_by_pnorm
(x, p=2, small_constant=1e-6)
attacks/ifgsm_attack.py:28
↓ 1 callersFunctionp_selection
Piece-wise constant schedule for p (the fraction of pixels changed on every iteration).
attacks/square.py:6
↓ 1 callersMethodproject_kern
(self, kern_size)
attacks/ifgsm_attack.py:236
↓ 1 callersMethodproject_noise
(self, x, stack_kern, kern_size)
attacks/ifgsm_attack.py:245
↓ 1 callersFunctionsave_data
()
data/data_prehandle.py:13
↓ 1 callersMethodschedule
(self, loss_prob, loss_cls, epoch)
trainners/MetaLearner.py:161
↓ 1 callersFunctionsize_to_str
(size)
utils/finetune.py:48
↓ 1 callersMethodsqueeze2d
(input, factor=2)
models/modules.py:322
↓ 1 callersMethodunsqueeze2d
(input, factor=2)
models/modules.py:334
↓ 1 callersFunctionupsample_dct_interface
(scale=4.)
models/cglow.py:67
↓ 1 callersMethodvalidate
(self)
trainners/Learner.py:78
FunctionDPN26
()
surro_models/cifar10_models/dpn.py:73
FunctionDenseNet161
()
surro_models/cifar10_models/densenet.py:95
FunctionDenseNet169
()
surro_models/cifar10_models/densenet.py:89
FunctionDenseNet201
()
surro_models/cifar10_models/densenet.py:92
FunctionPNASNetA
()
surro_models/cifar10_models/pnasnet.py:112
FunctionPreActResNet101
()
surro_models/cifar10_models/preact_resnet.py:106
FunctionPreActResNet152
()
surro_models/cifar10_models/preact_resnet.py:109
FunctionPreActResNet34
()
surro_models/cifar10_models/preact_resnet.py:100
FunctionPreActResNet50
()
surro_models/cifar10_models/preact_resnet.py:103
FunctionResNeXt29_32x4d
()
surro_models/cifar10_models/resnext.py:86
FunctionResNeXt29_4x64d
()
surro_models/cifar10_models/resnext.py:80
FunctionResNeXt29_8x64d
()
surro_models/cifar10_models/resnext.py:83
FunctionResNet101
()
surro_models/cifar10_models/resnet.py:109
FunctionResNet152
()
surro_models/cifar10_models/resnet.py:112
FunctionResNet34
()
surro_models/cifar10_models/resnet.py:103
FunctionResNet50
()
surro_models/cifar10_models/resnet.py:106
FunctionShuffleNetG3
()
surro_models/cifar10_models/shufflenet.py:94
Method__delattr__
(self, name: str)
utils/commons.py:54
Method__enter__
(self)
utils/commons.py:74
Method__exit__
(self, exc_type: Any, exc_value: Any, traceback: Any)
utils/commons.py:77
Method__getattr__
(self, name: str)
utils/commons.py:45
Method__getitem__
(self, idx)
data/datasets.py:40
Method__init__
(self, graph, optim, trainingset, validset, args, cuda)
trainners/Learner.py:11
Method__init__
(self, flow, adv_models, query_set_models, optim, train_set, valid_set, args, cuda)
trainners/MetaLearner.py:17
Method__init__
(self)
utils/surrogate_trainer.py:12
Method__init__
(self)
utils/load_models.py:173
Method__init__
(self, file_name: str = None, file_mode: str = "w", should_flush: bool = True)
utils/commons.py:61
Method__init__
(self, max_query)
utils/attack_count.py:5
Method__init__
(self, attack_method, uplimit=1, buffer_limit=200, batch_size=20)
utils/buffer.py:6
Method__init__
(self, batch_size)
utils/buffer.py:115
Method__init__
(self, root_dir)
data/datasets.py:16
Method__init__
( self, attack_method, surrogate_model, args, data
attacks/ifgsm_attack.py:36
Method__init__
( self, dataset_name, max_query, targeted, class_n
attacks/square.py:35
Method__init__
( self, dataset_name, max_query, targeted, class_n
attacks/cgattack.py:9
Method__init__
( self, dataset_name, max_query, targeted, class_n
attacks/base_attack.py:37
Method__init__
( self, dataset_name, max_query, targeted, class_n
attacks/signhunter.py:7
Method__init__
(self, inplanes, planes, stride=1, downsample=None)
surro_models/cifar10_models/pyramidnet.py:62
Method__init__
(self, dataset, depth, alpha, num_classes, bottleneck=False)
surro_models/cifar10_models/pyramidnet.py:115
Method__init__
(self)
surro_models/cifar10_models/lenet.py:6
Method__init__
(self, num_blocks, cardinality, bottleneck_width, num_classes=10)
surro_models/cifar10_models/resnext.py:41
Method__init__
(self, in_planes, out_planes, kernel_size, stride)
surro_models/cifar10_models/pnasnet.py:12
Method__init__
(self, in_planes, out_planes, stride=1)
surro_models/cifar10_models/pnasnet.py:41
Method__init__
(self, cell_type, num_cells, num_planes)
surro_models/cifar10_models/pnasnet.py:72
Method__init__
(self, in_planes, planes, stride=1)
surro_models/cifar10_models/senet.py:11
Method__init__
(self, in_planes, planes, stride=1)
surro_models/cifar10_models/senet.py:46
Method__init__
(self, last_planes, in_planes, out_planes, dense_depth, stride, first_layer)
surro_models/cifar10_models/dpn.py:8
Method__init__
(self, in_planes, growth_rate)
surro_models/cifar10_models/densenet.py:10
Method__init__
(self, in_planes, out_planes)
surro_models/cifar10_models/densenet.py:25
Method__init__
(self, nb_layers, in_planes, out_planes, block, stride, dropRate=0.0)
surro_models/cifar10_models/wrn.py:34
Method__init__
(self, depth, num_classes, widen_factor=1, dropRate=0.0)
surro_models/cifar10_models/wrn.py:46
Method__init__
(self, num_classes=10)
surro_models/cifar10_models/mobilenet.py:30
Method__init__
(self, groups=2)
surro_models/cifar10_models/shufflenetv2.py:11
Method__init__
(self, ratio)
surro_models/cifar10_models/shufflenetv2.py:23
Method__init__
(self, in_channels, split_ratio=0.5)
surro_models/cifar10_models/shufflenetv2.py:33
Method__init__
(self, net_size)
surro_models/cifar10_models/shufflenetv2.py:97
Method__init__
(self, vgg_name)
surro_models/cifar10_models/vgg.py:15
Method__init__
(self, groups)
surro_models/cifar10_models/shufflenet.py:11
Method__init__
(self, cfg)
surro_models/cifar10_models/shufflenet.py:52
Method__init__
(self, num_classes=10)
surro_models/cifar10_models/mobilenetv2.py:50
Method__init__
(self, in_planes, planes, stride=1)
surro_models/cifar10_models/preact_resnet.py:41
Method__init__
(self, block, num_blocks, num_classes=10)
surro_models/cifar10_models/preact_resnet.py:66
Method__init__
(self, in_planes, planes, stride=1)
surro_models/cifar10_models/resnet.py:17
Method__init__
(self, in_planes, planes, stride=1)
surro_models/cifar10_models/resnet.py:42
Method__init__
(self)
surro_models/cifar10_models/googlenet.py:57
Method__init__
(self, x_size, y_size, x_hidden_channels, x_hidden_size, y_hidden_channels)
models/cglow.py:11
Method__init__
(self, args)
models/cglow.py:202
Method__init__
(self, in_channel, out_channel, kernel_size=[3, 3], stride=[1, 1])
models/modules.py:49
Method__init__
(self, in_size, out_size)
models/modules.py:58
Method__init__
(self, in_channels, out_channels, kernel_size=[3, 3], stride=[1, 1])
models/modules.py:73
Method__init__
(self, x_size, y_channels, x_hidden_channels, x_hidden_size)
models/modules.py:82
Method__init__
(self, x_size, x_hidden_channels, x_hidden_size, y_channels)
models/modules.py:139
Method__init__
(self, in_channels, out_channels, kernel_size=[3, 3], stride=[1, 1])
models/modules.py:223
Method__init__
(self, in_channels, out_channels, kernel_size=[3, 3], stride=[1, 1])
models/modules.py:241
Method__init__
(self, x_size, y_size, hidden_channels)
models/modules.py:263
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