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Functions310 in github.com/chi0tzp/WarpedGANSpace

↓ 1 callersFunctionmain
A script for ranking the discovered non-linear paths in terms of correlation with a set of attributes. Options: =========================
rank_interpretable_paths.py:217
↓ 1 callersFunctionmain
A script for sampling from a pre-trained GAN latent space and generating images. The generated images, along with the corresponding latent codes (
sample_gan.py:32
↓ 1 callersFunctionmain
WarpedGANSpace -- Latent space traversal script. A script for traversing the latent space of a pre-trained GAN generator through paths defined by
traverse_latent_space.py:119
↓ 1 callersFunctionmake_dense
()
models/SNGAN/sn_gen_resnet.py:82
↓ 1 callersFunctionmake_final
()
models/SNGAN/sn_gen_resnet.py:88
↓ 1 callersFunctionmake_resnet_generator
(resnet_gen_config, img_size=128, channels=3, distribution=NormalDistribution(128))
models/SNGAN/sn_gen_resnet.py:81
↓ 1 callersFunctionmanual_bn
(x, gain=None, bias=None, return_mean_var=False, eps=1e-5)
models/BigGAN/layers.py:188
↓ 1 callersMethodmixed_classes
(self, batch_size)
models/gan_load.py:73
↓ 1 callersFunctionpower_iteration
(W, u_, update=True, eps=1e-12)
models/BigGAN/layers.py:25
↓ 1 callersFunctionproj
(x, y)
models/BigGAN/layers.py:13
↓ 1 callersMethodregister_slave
Register an slave device. Args: identifier: an identifier, usually is the device id. Returns: a `SlavePipe` obj
models/BigGAN/sync_batchnorm/comm.py:84
↓ 1 callersMethodreset_parameters
(self)
models/BigGAN/sync_batchnorm/batchnorm_reimpl.py:43
↓ 1 callersMethodreset_running_stats
(self)
models/BigGAN/sync_batchnorm/batchnorm_reimpl.py:39
↓ 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
models/BigGAN/sync_batchnorm/comm.py:102
↓ 1 callersMethodrun_slave
(self, msg)
models/BigGAN/sync_batchnorm/comm.py:49
↓ 1 callersMethodshortcut
(self, x)
models/BigGAN/layers.py:428
↓ 1 callersFunctiontensor2image
(tensor, adaptive=False)
sample_gan.py:13
↓ 1 callersFunctiontensor2image
(tensor, img_size=None, adaptive=False)
traverse_latent_space.py:26
↓ 1 callersMethodtensor_or_path_to_ndarray
Convert path (represented as a string) or torch.tensor to a numpy.ndarray Arguments: tensor_or_path {numpy.ndarray, torch.tens
lib/evaluation/sfd/core.py:138
↓ 1 callersMethodtrain
Training function. Args: generator : support_sets : reconstructor :
lib/trainer.py:129
FunctionIOU
(ax1, ay1, ax2, ay2, bx1, by1, bx2, by2)
lib/evaluation/sfd/bbox.py:17
FunctionPYBIND11_MODULE
models/StyleGAN2/op/fused_bias_act.cpp:19
FunctionPYBIND11_MODULE
models/StyleGAN2/op/upfirdn2d.cpp:21
Method__call__
(self, x)
lib/evaluation/au_detector/AU_detector.py:13
Method__getattr__
(self, name)
sample_gan.py:25
Method__getattr__
(self, name)
traverse_latent_space.py:14
Method__getattr__
(self, name)
lib/trainer.py:17
Method__getitem__
(self, index)
lib/data.py:17
Method__getstate__
(self)
models/BigGAN/sync_batchnorm/comm.py:78
Method__init__
(self, **kwargs)
traverse_attribute_space.py:33
Method__init__
(self, **kwargs)
traverse_latent_space.py:22
Method__init__
(self)
lib/aux.py:14
Method__init__
(self, reconstructor_type, dim, channels=3)
lib/reconstructor.py:11
Method__init__
SupportSets class constructor. Args: num_support_sets (int) : number of support sets (each one defining a warping function)
lib/support_sets.py:6
Method__init__
(self, root_path)
lib/data.py:10
Method__init__
(self, params=None, exp_dir=None, use_cuda=False, multi_gpu=False)
lib/trainer.py:25
Method__init__
(self)
lib/evaluation/archface/arcface.py:9
Method__init__
(self, channels, reduction)
lib/evaluation/archface/arcface.py:43
Method__init__
(self, in_channel, depth, stride)
lib/evaluation/archface/arcface.py:64
Method__init__
(self, in_channel, depth, stride)
lib/evaluation/archface/arcface.py:83
Method__init__
(self, inplanes, planes, stride=1, downsample=None)
lib/evaluation/celeba_attributes/celeba_attr_predictor.py:20
Method__init__
(self, inplanes, planes, stride=1, downsample=None)
lib/evaluation/celeba_attributes/celeba_attr_predictor.py:52
Method__init__
(self, inplanes, planes, drop_rate=0.15)
lib/evaluation/celeba_attributes/celeba_attr_predictor.py:88
Method__init__
(self, block, layers, num_bins)
lib/evaluation/hopenet/hopenet.py:6
Method__init__
(self, num_bins)
lib/evaluation/hopenet/hopenet.py:126
Method__init__
(self, device='cuda')
lib/evaluation/hopenet/pose_estimator.py:28
Method__init__
(self, npts=12, corenet='pretrained_models/disfa_adaptation_f0.pth', use_cuda=True)
lib/evaluation/au_detector/AU_detector.py:6
Method__init__
(self, au_model_path='models/pretrained/au_detector/disfa_adaptation_f0.pth', use_cuda=True)
lib/evaluation/au_detector/AU_detector.py:30
Method__init__
(self, in_planes, out_planes, lightweight = False)
lib/evaluation/au_detector/hourglass.py:18
Method__init__
(self, num_modules, depth, num_features, lightweight = False)
lib/evaluation/au_detector/hourglass.py:69
Method__init__
(self, num_modules=1, num_features = 128, n_points=66, block=ConvBlock)
lib/evaluation/au_detector/hourglass.py:210
Method__init__
(self, device, verbose)
lib/evaluation/sfd/core.py:19
Method__init__
(self, n_channels, scale=1.0)
lib/evaluation/sfd/net_s3fd.py:7
Method__init__
(self, path_to_detector=None, device="cuda", verbose=False)
lib/evaluation/sfd/sfd_detector.py:6
Method__init__
(self, G, target_classes=(239, ))
models/gan_load.py:66
Method__init__
(self, G)
models/gan_load.py:110
Method__init__
(self, G, shift_in_w_space)
models/gan_load.py:138
Method__init__
(self, in_channels, out_channels)
models/SNGAN/sn_gen_resnet.py:25
Method__init__
(self, model, out_img_shape, distribution)
models/SNGAN/sn_gen_resnet.py:58
Method__init__
(self, dim)
models/SNGAN/distribution.py:28
Method__init__
(self, G_ch=64, dim_z=128, bottom_width=4, resolution=128, G_kernel_size=3, G_attn='64', n_classes=1000,
models/BigGAN/BigGAN.py:55
Method__init__
(self, D_ch=64, D_wide=True, resolution=128, D_kernel_size=3, D_attn='64', n_classes=1000, num_D_SVs=1,
models/BigGAN/BigGAN.py:282
Method__init__
(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=Tr
models/BigGAN/layers.py:101
Method__init__
(self, in_features, out_features, bias=True, num_svs=1, num_itrs=1, eps=1e-12)
models/BigGAN/layers.py:114
Method__init__
(self, num_embeddings, embedding_dim, padding_idx=None, max_norm=None, norm_type=2, scale_grad
models/BigGAN/layers.py:126
Method__init__
(self, ch, which_conv=SNConv2d, name='attention')
models/BigGAN/layers.py:142
Method__init__
(self, num_channels, eps=1e-5, momentum=0.1)
models/BigGAN/layers.py:210
Method__init__
(self, output_size, input_size, which_linear, eps=1e-5, momentum=0.1, cross_replica=False, mybn
models/BigGAN/layers.py:276
Method__init__
(self, output_size, eps=1e-5, momentum=0.1, cross_replica=False, mybn=False)
models/BigGAN/layers.py:331
Method__init__
(self, in_channels, out_channels, which_conv=nn.Conv2d, which_bn=bn, activation=None,
models/BigGAN/layers.py:373
Method__init__
(self, in_channels, out_channels, which_conv=SNConv2d, wide=True, preactivation=False, activati
models/BigGAN/layers.py:410
Method__init__
(self)
models/BigGAN/sync_batchnorm/comm.py:21
Method__init__
(self, num_features, eps=1e-5, momentum=0.1)
models/BigGAN/sync_batchnorm/batchnorm_reimpl.py:27
Method__init__
(self, num_features, eps=1e-5, momentum=0.1, affine=True)
models/BigGAN/sync_batchnorm/batchnorm.py:39
Method__init__
(self)
models/StyleGAN2/model.py:10
Method__init__
(self, kernel, factor=2)
models/StyleGAN2/model.py:30
Method__init__
(self, kernel, factor=2)
models/StyleGAN2/model.py:49
Method__init__
(self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True)
models/StyleGAN2/model.py:85
Method__init__
(self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1.0, activation=None)
models/StyleGAN2/model.py:111
Method__init__
(self, negative_slope=0.2)
models/StyleGAN2/model.py:140
Method__init__
(self, in_channel, out_channel, kernel_size, style_dim, demodulate=True, upsample=False, down
models/StyleGAN2/model.py:149
Method__init__
(self)
models/StyleGAN2/model.py:232
Method__init__
(self, channel, size=4)
models/StyleGAN2/model.py:245
Method__init__
(self, in_channel, out_channel, kernel_size, style_dim, upsample=False, blur_kernel=[1, 3, 3, 1],
models/StyleGAN2/model.py:254
Method__init__
(self, in_channel, style_dim, upsample=True, blur_kernel=[1, 3, 3, 1])
models/StyleGAN2/model.py:271
Method__init__
(self, size, style_dim, n_mlp, channel_multiplier=2, blur_kernel=[1, 3, 3, 1], lr_mlp=0.01)
models/StyleGAN2/model.py:286
Method__init__
(self, in_channel, out_channel, kernel_size, downsample=False, blur_kernel=[1, 3, 3, 1], bias=True,
models/StyleGAN2/model.py:412
Method__init__
(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1])
models/StyleGAN2/model.py:443
Method__init__
(self, size, channel_multiplier=2, blur_kernel=[1, 3, 3, 1])
models/StyleGAN2/model.py:454
Method__init__
(self, channel, negative_slope=0.2, scale=2 ** 0.5)
models/StyleGAN2/op/fused_act.py:74
Method__init__
(self)
models/ProgGAN/model.py:13
Method__init__
(self, size)
models/ProgGAN/model.py:22
Method__init__
(self, in_channels, out_channels, kernel_size, padding)
models/ProgGAN/model.py:36
Method__init__
(self, in_channels, out_channels, kernel_size, padding)
models/ProgGAN/model.py:50
Method__len__
(self)
lib/data.py:14
Method__repr__
(self)
models/StyleGAN2/model.py:103
Method__repr__
(self)
models/StyleGAN2/model.py:133
Method__repr__
(self)
models/StyleGAN2/model.py:181
Method__setstate__
(self, state)
models/BigGAN/sync_batchnorm/comm.py:81
Method_check_input_dim
(self, input)
models/BigGAN/sync_batchnorm/batchnorm.py:281
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