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Functions329 in github.com/LetheSec/PLG-MI-Attack

⨍ Functions 329◇ Types & classes 88
↓ 12 callersFunctionget_block
(in_channel, depth, num_units, stride=2)
baselines/facenet.py:141
↓ 12 callersFunctionget_block
(in_channel, depth, num_units, stride=2)
baselines/evolve.py:98
↓ 12 callersFunctionget_block
(in_channel, depth, num_units, stride=2)
models/classifiers/evolve.py:98
↓ 9 callersFunctiondconv_bn_relu
(in_dim, out_dim)
baselines/generator.py:155
↓ 8 callersMethod__init__
(self, in_dim=3, dim=64)
baselines/discri.py:158
↓ 8 callersMethod__init__
(self, num_classes=1000)
baselines/classify.py:229
↓ 6 callersMethod__init__
(self, num_classes=1000)
baselines/facenet.py:10
↓ 6 callersMethod__init__
(self, n_classes)
models/classifiers/classifier.py:39
↓ 6 callersFunctionsave_tensor_images
(images, filename, nrow=None, normalize=True)
baselines/utils.py:173
↓ 5 callersMethod__init__
(self, in_dim=100, dim=64)
baselines/generator.py:6
↓ 4 callersMethod__init__
(self, channels, reduction)
baselines/evolve.py:24
↓ 4 callersMethod__init__
(self, channels, reduction)
models/classifiers/evolve.py:24
↓ 4 callersFunctionload_model_optim
Load trained weight. Args: checkpoint_path (str) model (nn.Module) optim (torch.optim) Returns: model
utils.py:207
↓ 4 callersFunctionlog_sum_exp
(x, axis=1)
baselines/utils.py:418
↓ 3 callersFunctiondis_hinge
(dis_fake, dis_real)
losses.py:38
↓ 3 callersFunctionlog_sum_exp
(x, axis=1)
baselines/KED_MI.py:50
↓ 3 callersFunctionset_random_seed
(seed=0)
reconstruct.py:21
↓ 3 callersFunctiontest
(model, criterion, dataloader)
baselines/engine.py:18
↓ 2 callersMethod__init__
(self, root='datasets/ffhq/thumbnails128x128/', transform=None)
top_n_selection.py:24
↓ 2 callersMethod__init__
(self, name, mode)
baselines/utils.py:27
↓ 2 callersMethod_preprocess
(self, dis_fake, dis_real)
losses.py:71
↓ 2 callersFunction_upsample
(x)
models/generators/resblocks.py:9
↓ 2 callersFunctionfreeze
(net)
baselines/KED_MI.py:25
↓ 2 callersFunctionfreeze
(net)
baselines/GMI.py:23
↓ 2 callersFunctionget_blocks
(num_layers)
baselines/facenet.py:145
↓ 2 callersFunctionget_blocks
(num_layers)
baselines/evolve.py:102
↓ 2 callersFunctionget_blocks
(num_layers)
models/classifiers/evolve.py:102
↓ 2 callersMethodget_processor
(self)
baselines/dataloader.py:48
↓ 2 callersMethodget_processor
(self)
baselines/dataloader.py:153
↓ 2 callersFunctioninit_dataloader
(args, file_path, batch_size=64, mode="gan", iterator=False, drop_last=True, name_list=None,
baselines/utils.py:90
↓ 2 callersFunctionload_json
(json_file)
baselines/utils.py:157
↓ 2 callersFunctionlow2high
(img)
baselines/utils.py:322
↓ 2 callersFunctionreparameterize
Reparameterization trick to sample from N(mu, var) from N(0,1). :param mu: (Tensor) Mean of the latent Gaussian [B x D] :param logvar
baselines/recovery.py:35
↓ 2 callersFunctionsample_from_data
Sample real images and labels from data_loader. Args: args (argparse object) device (torch.device) data_loader (DataLoade
dataset.py:9
↓ 2 callersFunctionsample_from_gen
Sample fake images and labels from generator. Args: args (argparse object) device (torch.device) num_classes (int): for p
dataset.py:28
↓ 2 callersFunctionsave_tensor_images
(images, filename, nrow=None, normalize=True)
utils.py:259
↓ 2 callersFunctionunfreeze
(net)
baselines/KED_MI.py:30
↓ 2 callersFunctionunfreeze
(net)
baselines/GMI.py:28
↓ 1 callersFunctionIR_50_112
Constructs a ir-50 model.
baselines/facenet.py:293
↓ 1 callersFunctionIR_50_64
Constructs a ir-50 model.
baselines/facenet.py:285
↓ 1 callersFunctionInfiniteSampler
(n)
dataset.py:87
↓ 1 callersMethod__init__
(self, num_features, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True)
links/conditional_batchnorm.py:9
↓ 1 callersMethod__init__
(self, num_features, num_classes, activation=F.relu, dim_emb=128)
models/discriminators/snresnet64.py:59
↓ 1 callersMethod__init__
(self, in_ch, out_ch, h_ch=None, ksize=3, pad=1, activation=F.relu, downsample=False)
models/discriminators/resblocks.py:12
↓ 1 callersMethod__iter__
(self)
dataset.py:105
↓ 1 callersMethod_initialize
(self)
links/conditional_batchnorm.py:53
↓ 1 callersMethod_initialize
(self)
models/discriminators/snresnet64.py:35
↓ 1 callersMethod_initialize
(self)
models/discriminators/snresnet64.py:82
↓ 1 callersMethod_initialize
(self)
models/discriminators/resblocks.py:32
↓ 1 callersMethod_initialize
(self)
models/discriminators/resblocks.py:68
↓ 1 callersMethod_initialize_weights
(self)
baselines/facenet.py:209
↓ 1 callersMethod_initialize_weights
(self)
baselines/facenet.py:267
↓ 1 callersMethod_initialize_weights
(self)
baselines/evolve.py:161
↓ 1 callersMethod_initialize_weights
(self)
baselines/evolve.py:225
↓ 1 callersMethod_initialize_weights
(self)
models/classifiers/evolve.py:161
↓ 1 callersMethod_initialize_weights
(self)
models/classifiers/evolve.py:225
↓ 1 callersFunctionadjust_learning_rate
decrease the learning rate
baselines/engine.py:132
↓ 1 callersFunctioncalc_fid
Calculate the FID of the reconstructed image. :param recovery_img_path: the dir of reconstructed images :param private_img_path: the dir
evaluation.py:15
↓ 1 callersFunctioncalc_fid
Calculate the FID of the reconstructed image. :param recovery_img_path: the dir of reconstructed images :param private_img_path: the dir
baselines/evaluation.py:14
↓ 1 callersFunctioncalc_knn
Get the KNN Dist from reconstructed images to private date :param feat: features of reconstructed images output by evaluation model :para
evaluation.py:104
↓ 1 callersFunctioncalc_knn
Get the KNN Dist from reconstructed images to private date :param feat: features of reconstructed images output by evaluation model :para
baselines/evaluation.py:66
↓ 1 callersFunctiondis_dcgan
(dis_fake, dis_real)
losses.py:48
↓ 1 callersFunctiondist_inversion
(G, D, T, E, iden, itr, lr=2e-2, momentum=0.9, lamda=100, iter_times=1500, clip_range=1, im
baselines/recovery.py:49
↓ 1 callersMethodflush
(self)
baselines/utils.py:42
↓ 1 callersMethodforward
(self, input, weight, bias, **kwargs)
links/conditional_batchnorm.py:15
↓ 1 callersFunctiongen_dcgan
(dis_fake, dis_real=None)
losses.py:53
↓ 1 callersFunctiongen_hinge
(dis_fake, dis_real=None)
losses.py:44
↓ 1 callersFunctionget_activations
Calculates the activations of the pool_3 layer for all images. Params: -- images : Numpy array of dimension (n_images, 3, hi, wi). The v
baselines/metrics/fid.py:8
↓ 1 callersFunctionget_activations
Calculates the activations of the pool_3 layer for all images. Params: -- images : Numpy array of dimension (n_images, 3, hi, wi). The v
metrics/fid.py:8
↓ 1 callersFunctionget_args
()
train_cgan.py:55
↓ 1 callersFunctionget_deprocessor
()
baselines/utils.py:314
↓ 1 callersFunctionget_knn_dist
Get KNN Dist of reconstructed images. :param E: :param infered_image_path: :param private_feats_path: :return:
evaluation.py:132
↓ 1 callersFunctionget_knn_dist
Get KNN Dist of reconstructed images. :param E: :param infered_image_path: :param private_feats_path: :return:
baselines/evaluation.py:94
↓ 1 callersMethodget_list
(self, file_path)
baselines/dataloader.py:130
↓ 1 callersFunctionget_logger
()
reconstruct.py:35
↓ 1 callersFunctionget_logger
()
baselines/recovery.py:24
↓ 1 callersFunctiongradient_penalty
(x, y)
baselines/GMI.py:33
↓ 1 callersFunctioninversion
(args, G, T, E, iden, itr, lr=2e-2, iter_times=1500, num_seeds=5)
reconstruct.py:46
↓ 1 callersFunctioninversion
(G, D, T, E, iden, itr, lr=2e-2, momentum=0.9, lamda=100, iter_times=1500, clip_range=1, improved=False,
baselines/recovery.py:173
↓ 1 callersMethodload_img
(self)
baselines/dataloader.py:143
↓ 1 callersFunctionload_my_state_dict
(self, state_dict)
baselines/utils.py:180
↓ 1 callersFunctionmain
()
train_cgan.py:131
↓ 1 callersFunctionmain
(args, model_name, trainloader, testloader)
baselines/train_classifier.py:21
↓ 1 callersFunctionprepare_results_dir
Makedir, init tensorboard if required, save args.
train_cgan.py:21
↓ 1 callersMethodresidual
(self, x)
models/discriminators/resblocks.py:48
↓ 1 callersMethodresidual
(self, x)
models/discriminators/resblocks.py:79
↓ 1 callersMethodresidual
(self, x, y=None, z=None, **kwargs)
models/generators/resblocks.py:59
↓ 1 callersFunctionrobust_test
(model, criterion, dataloader)
baselines/engine.py:145
↓ 1 callersFunctionsample_pseudo_labels
Sample pseudo-labels. Args: num_classes (int): number of classes in the dataset. batch_size (int): size of mini-batch. de
utils.py:74
↓ 1 callersFunctionsample_z
Sample random noises. Args: batch_size (int) dim_z (int) device (torch.device) distribution (str, optional): defa
utils.py:52
↓ 1 callersMethodshortcut
(self, x)
models/discriminators/resblocks.py:41
↓ 1 callersMethodshortcut
(self, x)
models/discriminators/resblocks.py:76
↓ 1 callersMethodshortcut
(self, x, **kwargs)
models/generators/resblocks.py:50
↓ 1 callersFunctionsoftXEnt
(input, target)
baselines/utils.py:425
↓ 1 callersFunctiontop_n_selection
Top-n selection strategy. :param args: top-n, save_path :param T: target model :param data_loader: dataloader of :return:
top_n_selection.py:120
FunctionIR_100
Constructs a ir-100 model.
baselines/evolve.py:259
FunctionIR_100
Constructs a ir-100 model.
models/classifiers/evolve.py:259
FunctionIR_101
Constructs a ir-101 model.
baselines/facenet.py:301
FunctionIR_152_112
Constructs a ir-152 model.
baselines/facenet.py:317
FunctionIR_152_112
Constructs a ir-152 model.
baselines/evolve.py:275
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