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

⨍ Functions 329◇ Types & classes 88
Functioncalc_center
(feat, iden, path="feature")
baselines/utils.py:387
Functioncalc_feat
(img)
baselines/utils.py:337
Functioncalc_knn
(feat, iden, path="feature")
baselines/utils.py:400
Functioncalc_psnr
(img1, img2)
baselines/utils.py:364
Functioncalculate_activation_statistics
Calculation of the statistics used by the FID. Params: -- images : Numpy array of dimension (n_images, 3, hi, wi). The values
baselines/metrics/fid.py:112
Functioncalculate_activation_statistics
Calculation of the statistics used by the FID. Params: -- images : Numpy array of dimension (n_images, 3, hi, wi). The values
metrics/fid.py:112
Functioncalculate_frechet_distance
Numpy implementation of the Frechet Distance. The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) and X_2 ~ N(mu_2, C_2
baselines/metrics/fid.py:59
Functioncalculate_frechet_distance
Numpy implementation of the Frechet Distance. The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) and X_2 ~ N(mu_2, C_2
metrics/fid.py:59
Functioncompletion_network_loss
(input, output, mask)
baselines/losses.py:5
Methodconv_ln_lrelu
(in_dim, out_dim, k, s, p)
baselines/discri.py:42
Methodconv_ln_lrelu
(in_dim, out_dim, k, s, p)
baselines/discri.py:82
Methodconv_ln_lrelu
(in_dim, out_dim)
baselines/discri.py:135
Methodconv_ln_lrelu
(in_dim, out_dim)
baselines/discri.py:161
Methodconv_ln_lrelu
(in_dim, out_dim)
baselines/discri.py:186
Functioncrop
* inputs: - x (torch.Tensor, required) A torch tensor of shape (N, C, H, W) is assumed. - area (sequence, require
baselines/utils.py:248
Functioncross_entropy_loss
(out, iden)
losses.py:16
Methoddconv_bn_relu
(in_dim, out_dim)
baselines/generator.py:9
Methoddconv_bn_relu
(in_dim, out_dim)
baselines/generator.py:38
Functionevaluate
Evaluate in the training process.
evaluation.py:173
Methodforward
(self, x)
baselines/facenet.py:19
Methodforward
(self, x)
baselines/facenet.py:44
Methodforward
(self, input)
baselines/facenet.py:55
Methodforward
(self, x)
baselines/facenet.py:81
Methodforward
(self, x)
baselines/facenet.py:105
Methodforward
(self, x)
baselines/facenet.py:130
Methodforward
(self, x)
baselines/facenet.py:203
Methodforward
(self, x)
baselines/facenet.py:260
Methodforward
(self, x)
baselines/utils.py:62
Methodforward
(self, x)
baselines/utils.py:441
Methodforward
(self, mask, gen, images)
baselines/losses.py:20
Methodforward
(self, out, gt)
baselines/losses.py:27
Methodforward
(self, x)
baselines/discri.py:19
Methodforward
(self, x)
baselines/discri.py:57
Methodforward
(self, x)
baselines/discri.py:96
Methodforward
(self, x)
baselines/discri.py:117
Methodforward
(self, x)
baselines/discri.py:148
Methodforward
(self, x)
baselines/discri.py:176
Methodforward
(self, x)
baselines/discri.py:199
Methodforward
(self, x)
baselines/discri.py:222
Methodforward
(self, input)
baselines/discri.py:251
Methodforward
(self, input)
baselines/classify.py:15
Methodforward
(self, x)
baselines/classify.py:28
Methodforward
(self, x)
baselines/classify.py:49
Methodforward
(self, x, mode="train")
baselines/classify.py:78
Methodforward
(self, out, gt, mode="reg")
baselines/classify.py:106
Methodforward
(self, out, gt)
baselines/classify.py:117
Methodforward
(self, x)
baselines/classify.py:138
Methodforward
(self, x)
baselines/classify.py:162
Methodforward
(self, x)
baselines/classify.py:186
Methodforward
(self, x)
baselines/classify.py:212
Methodforward
(self, x)
baselines/classify.py:245
Methodforward
(self, x)
baselines/classify.py:279
Methodforward
(self, input)
baselines/evolve.py:12
Methodforward
(self, x)
baselines/evolve.py:38
Methodforward
(self, x)
baselines/evolve.py:62
Methodforward
(self, x)
baselines/evolve.py:87
Methodforward
(self, x)
baselines/evolve.py:154
Methodforward
(self, x)
baselines/evolve.py:218
Methodforward
Get Inception feature maps Parameters ---------- inp : torch.autograd.Variable Input tensor of shape Bx3xHxW. Val
baselines/inception.py:108
Methodforward
(self, x)
baselines/generator.py:27
Methodforward
(self, x)
baselines/generator.py:55
Methodforward
(self, x)
baselines/generator.py:134
Methodforward
(self, x)
baselines/generator.py:207
Methodforward
(self, x)
baselines/generator.py:234
Methodforward
(self, inp)
baselines/generator.py:264
Methodforward
(self, input, c, **kwargs)
links/conditional_batchnorm.py:57
Methodforward
Get Inception feature maps Parameters ---------- inp : torch.autograd.Variable Input tensor of shape Bx3xHxW. Val
models/__init__.py:108
Methodforward
Get Inception feature maps Parameters ---------- inp : torch.autograd.Variable Input tensor of shape Bx3xHxW. Val
models/inception.py:108
Methodforward
(self, input)
models/classifiers/classifier.py:15
Methodforward
(self, x)
models/classifiers/classifier.py:28
Methodforward
(self, x)
models/classifiers/classifier.py:49
Methodforward
(self, x, mode="train")
models/classifiers/classifier.py:78
Methodforward
(self, out, gt, mode="reg")
models/classifiers/classifier.py:106
Methodforward
(self, out, gt)
models/classifiers/classifier.py:117
Methodforward
(self, x)
models/classifiers/classifier.py:138
Methodforward
(self, x)
models/classifiers/classifier.py:162
Methodforward
(self, x)
models/classifiers/classifier.py:186
Methodforward
(self, x)
models/classifiers/classifier.py:212
Methodforward
(self, input)
models/classifiers/evolve.py:12
Methodforward
(self, x)
models/classifiers/evolve.py:38
Methodforward
(self, x)
models/classifiers/evolve.py:62
Methodforward
(self, x)
models/classifiers/evolve.py:87
Methodforward
(self, x)
models/classifiers/evolve.py:154
Methodforward
(self, x)
models/classifiers/evolve.py:218
Methodforward
(self, x, y=None)
models/discriminators/snresnet64.py:41
Methodforward
(self, x, y=None)
models/discriminators/snresnet64.py:87
Methodforward
(self, x)
models/discriminators/resblocks.py:38
Methodforward
(self, x)
models/discriminators/resblocks.py:73
Methodforward
(self, z, y=None, **kwargs)
models/generators/resnet64.py:43
Methodforward
(self, x, y=None, z=None, **kwargs)
models/generators/resblocks.py:47
Functiongen_hole_area
* inputs: - size (sequence, required) A sequence of length 2 (W, H) is assumed. (W, H) is the size of hol
baselines/utils.py:229
Functiongenerate_images
Generate images. Priority: num_classes > class_id. Args: gen (nn.Module): generator. device (torch.device) batch_siz
utils.py:17
Functionget_center_mask
(img_size, bs)
baselines/utils.py:265
Functionget_model
(attack_name, classes)
baselines/utils.py:348
Functionget_private_feats
Get the features of private data on the evaluation model, and save as file. :param E: Evaluation model :param private_feats_path: save pa
evaluation.py:67
Functionget_train_mask
(img_size, bs)
baselines/utils.py:275
Functiongradient_penalty
(x, y)
baselines/KED_MI.py:35
Functionl2_norm
(input, axis=1)
baselines/facenet.py:59
Functionl2_norm
(input, axis=1)
baselines/evolve.py:16
Functionl2_norm
(input, axis=1)
models/classifiers/evolve.py:16
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