Functions — github.com/CausalLearning/robust-unlearnable-examples

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Functions131 in github.com/CausalLearning/robust-unlearnable-examples

⨍ Functions 131◇ Types & classes 26

↓ 6 callersMethodperturb

(self, model, criterion, x, y)

attacks/pgd_attacker.py:13

↓ 4 callersFunction_densenet

( arch: str, growth_rate: int, block_config: Tuple[int, int, int, int], num_init_features: int

models/densenet.py:220

↓ 4 callersMethod_make_layer

(self, block, planes, num_blocks, stride)

models/resnet.py:79

↓ 4 callersFunctionget_dataset

(dataset, root='./data', train=True, fitr=None)

utils/generic.py:86

↓ 3 callersMethod__getitem__

(self, idx)

utils/imagenet_utils.py:30

↓ 3 callersMethod__init__

( self, growth_rate: int = 32, block_config: Tuple[int, int, int, int] = (6, 12, 24, 1

models/densenet.py:154

↓ 3 callersMethod__init__

(self, num_blocks, in_dims, out_dims, wide=10)

models/resnet.py:110

↓ 3 callersMethod_make_layer

(self, block, planes, num_blocks, stride)

models/resnet.py:125

↓ 3 callersFunctionget_dataset

(dataset, root='./data', train=True)

utils/imagenet_utils.py:184

↓ 3 callersFunctionmake_layers

(cfg, in_dims=3, batch_norm=False)

models/vgg.py:48

↓ 2 callersMethod_clip_

(self, adv_x, x, radius)

attacks/robust_workers.py:136

↓ 2 callersMethod_clip_

(self, adv_x, x)

attacks/pgd_attacker.py:58

↓ 2 callersMethodaverage

(self)

utils/generic.py:27

↓ 2 callersMethodbn_function

(self, inputs: List[Tensor])

models/densenet.py:47

↓ 2 callersFunctiondatasetImageNet

(root='./data', train=True, transform=None)

utils/imagenet_utils.py:130

↓ 2 callersFunctionsave_checkpoint

(save_dir, save_name, model, optim, log)

train.py:53

↓ 2 callersFunctionsave_checkpoint

(save_dir, save_name, model, optim, log, def_noise=None)

generate_em.py:32

↓ 2 callersFunctionsave_checkpoint

(save_dir, save_name, model, optim, log, def_noise=None)

generate_robust_em.py:67

↓ 2 callersMethodupdate

(self, val, cnt)

utils/generic.py:22

↓ 1 callersMethod_get_adv_

(self, model, criterion, x, y)

attacks/robust_workers.py:113

↓ 1 callersMethodany_requires_grad

(self, input: List[Tensor])

models/densenet.py:53

↓ 1 callersMethodcall_checkpoint_bottleneck

(self, input: List[Tensor])

models/densenet.py:60

↓ 1 callersFunctiondatasetImageNetMini

(root='./data', train=True, transform=None)

utils/imagenet_utils.py:136

↓ 1 callersFunctionget_args

()

train.py:11

↓ 1 callersFunctionget_args

()

generate_tap.py:27

↓ 1 callersFunctionget_args

()

generate_em.py:11

↓ 1 callersFunctionget_args

()

generate_robust_em.py:11

↓ 1 callersFunctionget_filter

(fitr)

utils/imagenet_utils.py:171

↓ 1 callersFunctionget_filter

(fitr)

utils/generic.py:73

↓ 1 callersFunctionget_manual_loader

(dataset, data_path, batch_size)

train.py:42

↓ 1 callersFunctionget_transforms

(dataset, train=True, is_tensor=True)

utils/imagenet_utils.py:145

↓ 1 callersFunctionget_transforms

(dataset, train=True, is_tensor=True)

utils/generic.py:40

↓ 1 callersFunctionload_pretrained_model

(model, arch, pre_state_dict)

generate_robust_em.py:51

↓ 1 callersFunctionmain

init model / optim / dataloader / loss func

train.py:62

↓ 1 callersFunctionmain

init model / optim / loss func

generate_tap.py:72

↓ 1 callersFunctionmain

init model / optim / dataloader / loss func

generate_em.py:47

↓ 1 callersFunctionmain

init model / optim / loss func

generate_robust_em.py:84

↓ 1 callersFunctionmodel_state_to_cpu

(model_state)

utils/generic.py:315

↓ 1 callersMethodperturb

initialize noise

attacks/robust_workers.py:70

↓ 1 callersFunctionregenerate_def_noise

(def_noise, model, criterion, loader, defender, cpu, logger)

generate_tap.py:46

↓ 1 callersFunctionregenerate_def_noise

(def_noise, model, criterion, loader, defender, cpu)

generate_em.py:25

↓ 1 callersFunctionregenerate_def_noise

(def_noise, model, criterion, loader, defender, cpu)

generate_robust_em.py:59

↓ 1 callersFunctionsave_checkpoint

(save_dir, save_name, model, optim, log, def_noise=None)

generate_tap.py:57

Method__call__

(self, x)

utils/imagenet_utils.py:15

utils/imagenet_utils.py:47

Method__getitem__

(self, idx)

utils/imagenet_utils.py:72

Method__getitem__

(self, idx)

utils/imagenet_utils.py:98

(self, dataset, classes)

generate_tap.py:12

Method__init__

(self, trans=None)

utils/imagenet_utils.py:12

Method__init__

(self, dataset)

utils/imagenet_utils.py:21

Method__init__

(self, dataset)

utils/imagenet_utils.py:38

Method__init__

(self, dataset, noise, fitr=None)

utils/imagenet_utils.py:59

Method__init__

(self, dataset)

utils/imagenet_utils.py:89

Method__init__

(self, dataset, batch_size, shuffle=False, drop_last=False, num_workers=4)

utils/imagenet_utils.py:107

(self, x, y, transform=None, fitr=None)

utils/data.py:34

Method__init__

(self, x, y, transform=None)

utils/data.py:58

Method__init__

(self, dataset, batch_size, shuffle=False, drop_last=False, num_workers=4)

(self, samp_num, trans, radius, steps, step_size, random_start, ascending=True)

attacks/robust_workers.py:5

Method__init__

(self, samp_num, trans, radius, steps, step_size, random_start, atk_radius, atk_steps, atk_ste

attacks/robust_workers.py:54

Method__init__

(self, radius, steps, step_size, random_start, norm_type, ascending=True)

attacks/pgd_attacker.py:5

Method__init__

(self, samp_num, trans, radius, steps, step_size, random_start, ascending=True)

attacks/diff_aug_pgd.py:5

Method__init__

( self, num_input_features: int, growth_rate: int, bn_size: int, drop_

models/densenet.py:19

Method__init__

( self, num_layers: int, num_input_features: int, bn_size: int, growth

models/densenet.py:100

Method__init__

(self, num_input_features: int, num_output_features: int)

models/densenet.py:129

Method__init__

(self, features, out_channels)

models/vgg.py:18

Method__init__

(self, in_planes, planes, stride=1, wide=1)

models/resnet.py:13

Method__init__

(self, in_planes, planes, stride=1, wide=1)

models/resnet.py:39

Method__init__

(self, block, num_blocks, in_dims, out_dims, wide=1)

models/resnet.py:66

Method__iter__

(self)

utils/imagenet_utils.py:111

utils/imagenet_utils.py:33

Method__len__

(self)

utils/imagenet_utils.py:54

Method__len__

(self)

utils/imagenet_utils.py:84

Method__len__

(self)

utils/imagenet_utils.py:102

Method__len__

(self)

utils/imagenet_utils.py:114

utils/imagenet_utils.py:117

Functionadd_shared_args

models/densenet.py:61

FunctiondatasetCIFAR10

(root='./path', train=True, transform=None)

utils/data.py:74

FunctiondatasetCIFAR100

(root='./path', train=True, transform=None)

utils/data.py:78

FunctiondatasetTinyImageNet

(root='./path', train=True, transform=None)

utils/data.py:82

Functiondensenet121

r"""Densenet-121 model from `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_. The required minimum input s

models/densenet.py:235

Functiondensenet161

r"""Densenet-161 model from `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_. The required minimum input s

models/densenet.py:250

Functiondensenet169

r"""Densenet-169 model from `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_. The required minimum input s

models/densenet.py:265

Functiondensenet201

r"""Densenet-201 model from `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_. The required minimum input s

models/densenet.py:280

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