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Functions2,927 in github.com/G3H4N/GAN-Trained-Model-Detection-and-Attribution

⨍ Functions 2,927◇ Types & classes 307
↓ 1 callersFunctionDenormalize
(data)
models/GANStrctr_trainsets_real_multiclass_byMstr.py:172
↓ 1 callersFunctionDenormalize
(data)
models/CelebA_fake_GANship_Binary_classifier_train_sepVGG.py:131
↓ 1 callersFunctionDenormalize
(data)
models/CIFAR10_real_GANship_testsets_onlyVGG.py:173
↓ 1 callersFunctionDenormalize
(data)
models/CelebA_real_GANStrctr_trainsets.py:172
↓ 1 callersFunctionDenormalize
(data)
models/CIFAR10_backdoor_real_GANship_testsets.py:142
↓ 1 callersFunctionDenormalize
(data)
models/SVHN_real_GANStrctr_testsetsTGAN.py:183
↓ 1 callersFunctionDenormalize
(data)
models/CIFAR10_real_GANStrctr_trainsets_SVHN.py:173
↓ 1 callersFunctionDenormalize
(data)
models/Combine_real_GANStrctr_testsetsTGAN_FM.py:173
↓ 1 callersFunctionDenormalize
(data)
models/CelebA_real_GANship_classifier_train.py:130
↓ 1 callersFunctionDenormalize
(data)
models/model_train_specified.py:172
↓ 1 callersFunctionDenormalize
(data)
models/Noise_GANship_GANStrctr_testsets.py:171
↓ 1 callersFunctionDenormalize
(data)
models/SVHN_model_train_multi_temp.py:174
↓ 1 callersFunctionDenormalize
(data)
models/CIFAR10_model_train_multi_HALF_by_pretrained_GAN.py:172
↓ 1 callersFunctionDenormalize
(data)
models/CIFAR10_backdoor_real_GANship_trainsets.py:142
↓ 1 callersFunctionDenormalize
(data)
models/SVHN_real_FakeORReal_testsets.py:183
↓ 1 callersFunctionDenormalize
(data)
models/CelebA_real_GANStrctr_trainsets_noVGG.py:172
↓ 1 callersFunctionDenormalize
(data)
models/CIFAR10_Noise_GANship_trainsets.py:173
↓ 1 callersFunctionDenormalize
(data)
models/SVHN_fake_GANship_Binary_trainsets.py:184
↓ 1 callersFunctionDenormalize
(data)
models/SVHN_MNIST_GANship_testsets.py:173
↓ 1 callersFunctionDenormalize
(data)
models/CIFAR10_real_GANship_trainsets.py:173
↓ 1 callersFunctionDenormalize
(data)
models/Combine_real_GANStrctr_trainsets_FM.py:173
↓ 1 callersFunctionDenormalize
(data)
models/SVHN_real_GANship_testsets_labelonly.py:176
↓ 1 callersFunctionDenormalize
(data)
models/model_train_multi_real.py:172
↓ 1 callersFunctionDenormalize
(data)
models/CelebA_real_GANship_trainsets.py:172
↓ 1 callersFunctionDenormalize
(data)
models/SVHN_real_GANship_testsets.py:183
↓ 1 callersFunctionDenormalize
(data)
models/FMNIST_Noise_GANship_trainsets.py:173
↓ 1 callersFunctionDenormalize
(data)
models/posterio_GANship_trainsets_real_multiclass.py:172
↓ 1 callersFunctionDenormalize
(data)
models/CelebA_FFHQ_GANship_testsets.py:172
↓ 1 callersFunctionDenormalize
(data)
models/GANStrctr_testsets_real_multiclass.py:172
↓ 1 callersFunctionDenormalize
(data)
models/FMNIST_real_GANStrctr_testsetsTGAN.py:173
↓ 1 callersFunctionDenormalize
(data)
models/CIFAR10_real_GANStrctr_testsetsTGAN_FM.py:173
↓ 1 callersFunctionDenormalize
(data)
models/Noise_GANStrctr_testsetsTGAN.py:172
↓ 1 callersFunctionDenormalize
(data)
models/CelebA_fake_GANship_Binary_testsets.py:173
↓ 1 callersFunctionDenormalize
(data)
models/CIFAR10_real_GANStrctr_testsets_CelebA.py:173
↓ 1 callersFunctionDenormalize
(data)
models/CIFAR10_real_GANship_testsets.py:173
↓ 1 callersFunctionDenormalize
(data)
models/CelebA_real_GANship_trainsets_noVGG.py:172
↓ 1 callersFunctionDenormalize
(data)
models/classifier_train_strctr.py:179
↓ 1 callersFunctionDenormalize
(data)
models/CIFAR10_real_GANship_classifier_train.py:130
↓ 1 callersFunctionDenormalize
(data)
models/SVHN_Noise_GANship_testsets.py:173
↓ 1 callersFunctionDenormalize
(data)
models/SVHN_MNIST_GANship_trainsets.py:173
↓ 1 callersFunctionDenormalize
(data)
models/SVHN_real_GANStrctr_testsets.py:183
↓ 1 callersFunctionDenormalize
(data)
models/posterio_GANship_testsets_together_binaryclassify_multimge.py:172
↓ 1 callersFunctionDenormalize
(data)
models/FMNIST_Noise_GANStrctr_testsetsTGAN.py:172
↓ 1 callersFunctionDenormalize
(data)
models/GANStrctr_testsets_real_multiclass_byMstr.py:172
↓ 1 callersFunctionDenormalize
(data)
models/FMNIST_Noise_GANship_testsets.py:173
↓ 1 callersFunctionDenormalize
(data)
data_processing/show_t-sne_boundary_data2.py:126
↓ 1 callersFunctionEfficientNetB0
()
models/structures/efficientnet.py:154
↓ 1 callersFunctionGenerateDataSet
(gan_path, z_dim, c_dim, dataset_size_perclass, dataset_path)
models/posterio_GANship_trainsets_together_binaryclassify_multimge.py:175
↓ 1 callersFunctionGenerateDataSet
(gan_path, z_dim, c_dim, dataset_size_perclass, dataset_path)
models/CelebA_fake_GANship_Binary_trainsets_noVGG.py:176
↓ 1 callersFunctionGenerateDataSet
(gan_path, z_dim, c_dim, dataset_size_perclass, dataset_path)
models/posterio_GANship_testsetsTGAN_together_binaryclassify_multimge_byMstr.py:175
↓ 1 callersFunctionGenerateDataSet
(gan_path, z_dim, c_dim, dataset_size_perclass, dataset_path)
models/FMNIST_fake_GANship_Binary_trainsets.py:177
↓ 1 callersFunctionGenerateDataSet
(gan_path, z_dim, c_dim, dataset_size_perclass, dataset_path)
models/FMNIST_fake_GANship_Binary_testsets.py:177
↓ 1 callersFunctionGenerateDataSet
(gan_path, z_dim, c_dim, dataset_size_perclass, dataset_path)
models/posterio_GANship_trainsets_together_binaryclassify_multimge_byMstr.py:175
↓ 1 callersFunctionGenerateDataSet
(gan_path, z_dim, c_dim, dataset_size_perclass, dataset_path)
models/posterio_GANship_trainsets_together.py:175
↓ 1 callersFunctionGenerateDataSet
(gan_path, z_dim, c_dim, dataset_size_perclass, dataset_path)
models/CelebA_fake_GANship_Binary_trainsets.py:176
↓ 1 callersFunctionGenerateDataSet
(gan_path, z_dim, c_dim, dataset_size_perclass, dataset_path)
models/accuracy_on_fake.py:180
↓ 1 callersFunctionGenerateDataSet
(gan_path, z_dim, c_dim, dataset_size_perclass, dataset_path)
models/SVHN_fake_GANship_Binary_testsets.py:187
↓ 1 callersFunctionGenerateDataSet
(gan_path, z_dim, c_dim, dataset_size_perclass, dataset_path)
models/posterio_GANship_testsetsTGAN_together_binaryclassify_multimge.py:175
↓ 1 callersFunctionGenerateDataSet
(gan_path, z_dim, c_dim, dataset_size_perclass, dataset_path)
models/posterio_GANship_testsets_together_binaryclassify_multimge_byMstr.py:175
↓ 1 callersFunctionGenerateDataSet
(gan_path, z_dim, c_dim, dataset_size_perclass, dataset_path)
models/CelebA_fake_GANship_Binary_testsets_onlyVGG.py:176
↓ 1 callersFunctionGenerateDataSet
(gan_path, z_dim, c_dim, dataset_size_perclass, dataset_path)
models/SVHN_fake_GANship_Binary_trainsets.py:187
↓ 1 callersFunctionGenerateDataSet
(gan_path, z_dim, c_dim, dataset_size_perclass, dataset_path)
models/CelebA_fake_GANship_Binary_testsets.py:176
↓ 1 callersFunctionGenerateDataSet
(gan_path, z_dim, c_dim, dataset_size_perclass, dataset_path)
models/posterio_GANship_testsets_together_binaryclassify_multimge.py:175
↓ 1 callersFunctionGenerateDataSet
(gan_path, z_dim, c_dim, dataset_size_perclass, dataset_path)
data_processing/show_t-sne_boundary_data2.py:128
↓ 1 callersFunctionMaxMinNormalization2D
(x)
models/CIFAR10_real_GANStrctr_trainsets_CelebA.py:312
↓ 1 callersFunctionMaxMinNormalization2D
(x)
models/CIFAR10_real_GANStrctr_testsets_FM.py:312
↓ 1 callersFunctionMaxMinNormalization2D
(x)
models/CIFAR10_real_GANStrctr_trainsets_FM.py:312
↓ 1 callersFunctionMaxMinNormalization2D
(x)
models/Noise_GANStrctr_trainsets.py:311
↓ 1 callersFunctionMaxMinNormalization2D
(x)
models/CIFAR10_real_GANStrctr_testsetsTGAN_CelebA.py:312
↓ 1 callersFunctionMaxMinNormalization2D
(x)
models/Noise_GANStrctr_testsets.py:311
↓ 1 callersFunctionMaxMinNormalization2D
(x)
models/CIFAR10_real_GANStrctr_trainsets_SVHN.py:312
↓ 1 callersFunctionMaxMinNormalization2D
(x)
models/CIFAR10_real_GANStrctr_testsetsTGAN_FM.py:312
↓ 1 callersFunctionMaxMinNormalization2D
(x)
models/Noise_GANStrctr_testsetsTGAN.py:311
↓ 1 callersFunctionMaxMinNormalization2D
(x)
models/CIFAR10_real_GANStrctr_testsets_CelebA.py:312
↓ 1 callersFunctionPNASNetB
()
models/structures/pnasnet.py:115
↓ 1 callersFunctionPreActResNet18
()
models/structures/preact_resnet.py:97
↓ 1 callersFunctionRegNetX_200MF
()
models/structures/regnet.py:110
↓ 1 callersFunctionResNeXt29_2x64d
()
models/structures/resnext.py:77
↓ 1 callersFunctionResNet18
(c_dim=8)
models/structures/resnet.py:108
↓ 1 callersFunctionSENet18
()
models/structures/senet.py:112
↓ 1 callersFunctionShuffleNetG2
()
models/structures/shufflenet.py:86
↓ 1 callersMethod__init__
(self, nch_info)
models/Compare/networks.py:7
↓ 1 callersMethod__init__
(self, in_planes, cardinality=32, bottleneck_width=4, stride=1)
models/structures/resnext.py:14
↓ 1 callersMethod__init__
(self, cfg)
models/structures/dpn.py:39
↓ 1 callersMethod__init__
(self, in_planes, out_planes, stride=1)
models/structures/mobilenet.py:13
↓ 1 callersMethod__init__
(self, in_planes, out_planes, expansion, stride)
models/structures/mobilenetv2.py:13
↓ 1 callersMethod__init__
(self, in_planes, n1x1, n3x3red, n3x3, n5x5red, n5x5, pool_planes)
models/structures/googlenet.py:8
↓ 1 callersMethod_make_layers
(self, in_planes)
models/structures/mobilenet.py:37
↓ 1 callersMethod_make_layers
(self, cfg)
models/structures/vgg.py:26
↓ 1 callersMethod_make_layers
(self, in_channels)
models/structures/efficientnet.py:121
↓ 1 callersMethod_make_layers
(self, in_planes)
models/structures/mobilenetv2.py:60
↓ 1 callersFunctionblah
()
GANs/pylib/timer.py:118
↓ 1 callersFunctiondensenet_cifar
()
models/structures/densenet.py:98
↓ 1 callersFunctiondrop_connect
(x, drop_ratio)
models/structures/efficientnet.py:16
↓ 1 callersFunctionfit
Loaders, model, loss function and metrics should work together for a given task, i.e. The model should be able to process data output of load
models/Compare/trainer1.py:5
↓ 1 callersMethodflush
(self)
models/train.py:27
↓ 1 callersFunctionloadData
(filename)
GANs/train_DCGAN_wgan-gp.py:93
↓ 1 callersFunctionloadData
(filename)
GANs/train_CGAN.py:93
↓ 1 callersFunctionloadData
(filename)
GANs/train_PCGAN.py:93
↓ 1 callersFunctionloadData
(filename)
GANs/train_DCGAN.py:93
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