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github.com/ajbrock/BigGAN-PyTorch
/ types & classes
Types & classes
52 in github.com/ajbrock/BigGAN-PyTorch
⨍
Functions
231
◇
Types & classes
52
↓ 22 callers
Class
GBlock
TFHub/biggan_v1.py:131
↓ 20 callers
Class
SpectralNorm
TFHub/biggan_v1.py:27
↓ 4 callers
Class
SelfAttention
Self Attention Layer
TFHub/biggan_v1.py:81
↓ 3 callers
Class
Distribution
utils.py:1059
↓ 2 callers
Class
ConditionalBatchNorm2d
TFHub/biggan_v1.py:115
↓ 2 callers
Class
myBN
layers.py:212
↓ 1 callers
Class
CallbackContext
sync_batchnorm/replicate.py:23
↓ 1 callers
Class
CenterCropLongEdge
Crops the given PIL Image on the long edge. Args: size (sequence or int): Desired output size of the crop. If size is an int instead
utils.py:434
↓ 1 callers
Class
DBlock
BigGANdeep.py:283
↓ 1 callers
Class
FutureResult
A thread-safe future implementation. Used only as one-to-one pipe.
sync_batchnorm/comm.py:18
↓ 1 callers
Class
GBlock
BigGANdeep.py:23
↓ 1 callers
Class
MultiEpochSampler
r"""Samples elements randomly over multiple epochs Arguments: data_source (Dataset): dataset to sample from num_epochs (int) : Number o
utils.py:481
↓ 1 callers
Class
RandomCropLongEdge
Crops the given PIL Image on the long edge with a random start point. Args: size (sequence or int): Desired output size of the crop. If size i
utils.py:453
↓ 1 callers
Class
SlavePipe
Pipe for master-slave communication.
sync_batchnorm/comm.py:46
↓ 1 callers
Class
SyncMaster
An abstract `SyncMaster` object. - During the replication, as the data parallel will trigger an callback of each module, all slave devices should
sync_batchnorm/comm.py:56
↓ 1 callers
Class
TFHub2Pytorch
TFHub/converter.py:80
↓ 1 callers
Class
WrapInception
inception_utils.py:27
Class
Adam16
utils.py:1128
Class
Attention
layers.py:144
Class
BatchNorm2dReimpl
A re-implementation of batch normalization, used for testing the numerical stability. Author: acgtyrant See also: https://github
sync_batchnorm/batchnorm_reimpl.py:18
Class
CIFAR10
datasets.py:243
Class
CIFAR100
datasets.py:351
Class
DBlock
layers.py:412
Class
DataParallelWithCallback
Data Parallel with a replication callback. An replication callback `__data_parallel_replicate__` of each module will be invoked after being
sync_batchnorm/replicate.py:50
Class
Discriminator
BigGAN.py:283
Class
Discriminator
BigGANdeep.py:359
Class
Discriminator
TFHub/biggan_v1.py:339
Class
GBlock
layers.py:375
Class
G_D
BigGAN.py:408
Class
G_D
BigGANdeep.py:492
Class
Generator
BigGAN.py:54
Class
Generator
BigGANdeep.py:96
Class
Generator128
TFHub/biggan_v1.py:197
Class
Generator256
TFHub/biggan_v1.py:244
Class
Generator512
TFHub/biggan_v1.py:290
Class
ILSVRC_HDF5
datasets.py:184
Class
ImageFolder
A generic data loader where the images are arranged in this way: :: root/dogball/xxx.png root/dogball/xxy.png root/dogball/xxz.
datasets.py:82
Class
MetricsLogger
utils.py:744
Class
MyLogger
utils.py:771
Class
SN
layers.py:60
Class
SNConv2d
layers.py:103
Class
SNEmbedding
layers.py:128
Class
SNLinear
layers.py:116
Class
SynchronizedBatchNorm1d
r"""Applies Synchronized Batch Normalization over a 2d or 3d input that is seen as a mini-batch. .. math:: y = \frac{x - mean[x]}{ \
sync_batchnorm/batchnorm.py:162
Class
SynchronizedBatchNorm2d
r"""Applies Batch Normalization over a 4d input that is seen as a mini-batch of 3d inputs .. math:: y = \frac{x - mean[x]}{ \sqrt{Va
sync_batchnorm/batchnorm.py:225
Class
SynchronizedBatchNorm3d
r"""Applies Batch Normalization over a 5d input that is seen as a mini-batch of 4d inputs .. math:: y = \frac{x - mean[x]}{ \sqrt{Va
sync_batchnorm/batchnorm.py:288
Class
TorchTestCase
sync_batchnorm/unittest.py:15
Class
_SynchronizedBatchNorm
sync_batchnorm/batchnorm.py:38
Class
bn
layers.py:333
Class
ccbn
layers.py:278
Class
ema
utils.py:614
Class
identity
layers.py:54