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Functions231 in github.com/ajbrock/BigGAN-PyTorch

⨍ Functions 231◇ Types & classes 52
↓ 1 callersMethodrun_master
Main entry for the master device in each forward pass. The messages were first collected from each devices (including the master devi
sync_batchnorm/comm.py:102
↓ 1 callersMethodrun_slave
(self, msg)
sync_batchnorm/comm.py:49
↓ 1 callersMethodshortcut
(self, x)
layers.py:431
↓ 1 callersMethodshortcut
(self, x)
BigGANdeep.py:308
↓ 1 callersFunctionsqrt_newton_schulz
(A, numIters, dtype=None)
inception_utils.py:124
↓ 1 callersFunctiontorch_calculate_frechet_distance
Pytorch implementation of the Frechet Distance. Taken from https://github.com/bioinf-jku/TTUR The Frechet distance between two multivariate Gaussi
inception_utils.py:200
↓ 1 callersFunctiontorch_cov
Estimate a covariance matrix given data. Covariance indicates the level to which two variables vary together. If we examine N-dimensional sam
inception_utils.py:89
↓ 1 callersFunctiontrain
(x, y)
train_fns.py:15
FunctionGAN_training_function
(G, D, GD, z_, y_, ema, state_dict, config)
train_fns.py:20
Method__call__
Args: img (PIL Image): Image to be cropped. Returns: PIL Image: Cropped image.
utils.py:441
Method__call__
Args: img (PIL Image): Image to be cropped. Returns: PIL Image: Cropped image.
utils.py:460
Method__getitem__
Args: index (int): Index Returns: tuple: (image, target) where target is class_index of the target class.
datasets.py:143
Method__getitem__
Args: index (int): Index Returns: tuple: (image, target) where target is class_index of the target class.
datasets.py:208
Method__getitem__
Args: index (int): Index Returns: tuple: (image, target) where target is index of the target class.
datasets.py:326
Method__getstate__
(self)
sync_batchnorm/comm.py:78
Method__init__
(self, G_ch=64, dim_z=128, bottom_width=4, resolution=128, G_kernel_size=3, G_attn='64', n_clas
BigGAN.py:55
Method__init__
(self, D_ch=64, D_wide=True, resolution=128, D_kernel_size=3, D_attn='64', n_classes=1000,
BigGAN.py:285
Method__init__
(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=Tr
layers.py:104
Method__init__
(self, in_features, out_features, bias=True, num_svs=1, num_itrs=1, eps=1e-12)
layers.py:117
Method__init__
(self, num_embeddings, embedding_dim, padding_idx=None, max_norm=None, norm_type=2, scale_grad
layers.py:129
Method__init__
(self, ch, which_conv=SNConv2d, name='attention')
layers.py:145
Method__init__
(self, num_channels, eps=1e-5, momentum=0.1)
layers.py:213
Method__init__
(self, output_size, input_size, which_linear, eps=1e-5, momentum=0.1, cross_replica=False, mybn
layers.py:279
Method__init__
(self, output_size, eps=1e-5, momentum=0.1, cross_replica=False, mybn=False)
layers.py:334
Method__init__
(self, in_channels, out_channels, which_conv=nn.Conv2d, which_bn=bn, activation=None,
layers.py:376
Method__init__
(self, in_channels, out_channels, which_conv=SNConv2d, wide=True, preactivation=False, activati
layers.py:413
Method__init__
(self, data_source, num_epochs, start_itr=0, batch_size=128)
utils.py:490
Method__init__
(self, fname, reinitialize=False)
utils.py:745
Method__init__
(self, fname, reinitialize=False, logstyle='%3.3f')
utils.py:772
Method__init__
(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,weight_decay=0)
utils.py:1129
Method__init__
(self, in_channels, out_channels, which_conv=nn.Conv2d, which_bn=layers.bn, activation=None,
BigGANdeep.py:24
Method__init__
(self, G_ch=64, G_depth=2, dim_z=128, bottom_width=4, resolution=128, G_kernel_size=3, G_attn='
BigGANdeep.py:97
Method__init__
(self, in_channels, out_channels, which_conv=layers.SNConv2d, wide=True, preactivation=True, ac
BigGANdeep.py:284
Method__init__
(self, D_ch=64, D_wide=True, D_depth=2, resolution=128, D_kernel_size=3, D_attn='64', n_classes
BigGANdeep.py:361
Method__init__
(self, root, transform=None, target_transform=None, loader=default_loader, load_in_mem=False,
datasets.py:107
Method__init__
(self, root, transform=None, target_transform=None, load_in_mem=False, train=True,download=Fal
datasets.py:185
Method__init__
(self, root, train=True, transform=None, target_transform=None, download=True, validat
datasets.py:245
Method__init__
(self, net)
inception_utils.py:28
Method__init__
(self, state_dict, tf_weights, resolution=256, load_ema=True, verbose=False)
TFHub/converter.py:97
Method__init__
(self, module, name='weight', power_iterations=1)
TFHub/biggan_v1.py:28
Method__init__
(self, in_dim, activation=F.relu)
TFHub/biggan_v1.py:84
Method__init__
(self, num_features, num_classes, eps=1e-4, momentum=0.1)
TFHub/biggan_v1.py:116
Method__init__
(self, code_dim=120, n_class=1000, chn=96, debug=False)
TFHub/biggan_v1.py:198
Method__init__
(self, code_dim=140, n_class=1000, chn=96, debug=False)
TFHub/biggan_v1.py:245
Method__init__
(self, code_dim=128, n_class=1000, chn=96, debug=False)
TFHub/biggan_v1.py:291
Method__init__
(self, n_class=1000, chn=96, debug=False)
TFHub/biggan_v1.py:340
Method__init__
(self)
sync_batchnorm/comm.py:21
Method__init__
(self, num_features, eps=1e-5, momentum=0.1)
sync_batchnorm/batchnorm_reimpl.py:27
Method__init__
(self, num_features, eps=1e-5, momentum=0.1, affine=True)
sync_batchnorm/batchnorm.py:39
Method__iter__
(self)
utils.py:501
Method__len__
(self)
utils.py:519
Method__len__
(self)
datasets.py:238
Method__len__
(self)
datasets.py:347
Method__repr__
(self)
utils.py:450
Method__repr__
(self)
utils.py:475
Method__repr__
(self)
datasets.py:169
Method__setstate__
(self, state)
sync_batchnorm/comm.py:81
Method_check_input_dim
(self, input)
sync_batchnorm/batchnorm.py:281
Method_check_input_dim
(self, input)
sync_batchnorm/batchnorm.py:345
Method_data_parallel_master
Reduce the sum and square-sum, compute the statistics, and broadcast it.
sync_batchnorm/batchnorm.py:120
Function_progress
(count, block_size, total_size)
inception_tf13.py:84
Functionaccumulate_standing_stats
(net, z, y, nclasses, num_accumulations=16)
utils.py:1108
Functionadd_sample_parser
(parser)
utils.py:366
MethodassertTensorClose
(self, x, y)
sync_batchnorm/unittest.py:16
Functioncount_parameters
(module)
utils.py:1041
Functiondefault_loader
(path)
datasets.py:74
Functiondefault_ortho
(model, strength=1e-4, blacklist=[])
utils.py:661
Functiondenorm
(x)
TFHub/biggan_v1.py:22
Functiondummy_training_function
()
train_fns.py:14
Methodextra_repr
(self)
layers.py:326
Methodforward
(self, z, y)
BigGAN.py:230
Methodforward
(self, x, y=None)
BigGAN.py:391
Methodforward
(self, z, gy, x=None, dy=None, train_G=False, return_G_z=False, split_D=False)
BigGAN.py:414
Methodforward
(self, input)
layers.py:55
Methodforward
(self, x)
layers.py:110
Methodforward
(self, x)
layers.py:121
Methodforward
(self, x)
layers.py:137
Methodforward
(self, x, y=None)
layers.py:156
Methodforward
(self, x, gain, bias)
layers.py:234
Methodforward
(self, x, y)
layers.py:306
Methodforward
(self, x, y=None)
layers.py:359
Methodforward
(self, x, y)
layers.py:398
Methodforward
(self, x)
layers.py:444
Methodforward
(self, x, y)
BigGANdeep.py:48
Methodforward
(self, z, y)
BigGANdeep.py:265
Methodforward
(self, x)
BigGANdeep.py:315
Methodforward
(self, x, y=None)
BigGANdeep.py:475
Methodforward
(self, z, gy, x=None, dy=None, train_G=False, return_G_z=False, split_D=False)
BigGANdeep.py:498
Methodforward
(self, x)
inception_utils.py:35
Methodforward
(self, *args)
TFHub/biggan_v1.py:76
Methodforward
(self, x)
TFHub/biggan_v1.py:98
Methodforward
(self, x, y)
TFHub/biggan_v1.py:123
Methodforward
(self, input, class_id)
TFHub/biggan_v1.py:226
Methodforward
(self, input, class_id)
TFHub/biggan_v1.py:272
Methodforward
(self, input, class_id)
TFHub/biggan_v1.py:321
Methodforward
(self, input, class_id)
TFHub/biggan_v1.py:376
Methodforward
(self, input_)
sync_batchnorm/batchnorm_reimpl.py:48
Methodforward
(self, input, gain=None, bias=None)
sync_batchnorm/batchnorm.py:48
Functionget_SVs
(net, prefix)
utils.py:968
Functionget_data_loaders
(dataset, data_root=None, augment=False, batch_size=64, num_workers=8, shuffle=True, loa
utils.py:524
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