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Functions284 in github.com/SHShim0513/SD-VITON

⨍ Functions 284◇ Types & classes 77
↓ 1 callersFunctiontensor2np
(tensor_obj)
eval_models/__init__.py:62
↓ 1 callersFunctiontest
(opt, test_loader, board, tocg, generator)
test_generator.py:93
↓ 1 callersFunctiontrain
(opt, train_loader, tocg, D)
train_condition.py:76
↓ 1 callersFunctiontrain
Train Generator
train_generator.py:129
FunctionDiffAugment
(x, policy='', channels_first=True)
pg_modules/diffaug.py:9
FunctionUpBlockBig
(in_planes, out_planes)
pg_modules/blocks.py:93
FunctionUpBlockSmall
(in_planes, out_planes)
pg_modules/blocks.py:65
Method__call__
(self, input, label, for_discriminator)
network_generator.py:341
Method__call__
(self, input, target_is_real, for_discriminator=True)
network_generator.py:427
Method__call__
(self, input, target_is_real)
networks.py:373
Method__getitem__
(self, index)
cp_dataset_test.py:116
Method__getitem__
(self, index)
cp_dataset.py:125
Method__getstate__
(self)
sync_batchnorm/comm.py:78
Method__init__
(self, opt)
cp_dataset_test.py:16
Method__init__
(self)
network_generator.py:10
Method__init__
(self, norm_nc)
network_generator.py:53
Method__init__
(self, norm_type, norm_nc, label_nc)
network_generator.py:76
Method__init__
(self, opt, input_nc, output_nc, use_mask_norm=True)
network_generator.py:122
Method__init__
(self, opt, input_nc)
network_generator.py:173
Method__init__
(self, opt)
network_generator.py:261
Method__init__
(self, opt)
network_generator.py:302
Method__init__
(self, tensor=torch.FloatTensor)
network_generator.py:337
Method__init__
(self, opt, input1_nc, input2_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, num_layers=5)
networks.py:14
Method__init__
(self, in_nc, out_nc, scale='down', norm_layer=nn.BatchNorm2d)
networks.py:261
Method__init__
(self, layids = None)
networks.py:324
Method__init__
(self, use_lsgan=True, target_real_label=1.0, target_fake_label=0.0, tensor=torch.FloatTensor
networks.py:343
Method__init__
(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, num_D=3, g
networks.py:387
Method__init__
(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, getIntermFeat=False, Ddropo
networks.py:436
Method__init__
(self, opt, dataset)
cp_dataset.py:278
Method__init__
(self)
eval_models/base_model.py:8
Method__init__
(self, model='net-lin', net='alex', colorspace='rgb', spatial=False, use_gpu=True, gpu_ids=[0])
eval_models/__init__.py:14
Method__init__
(self, requires_grad=False, pretrained=True)
eval_models/pretrained_networks.py:7
Method__init__
(self, requires_grad=False, pretrained=True)
eval_models/pretrained_networks.py:58
Method__init__
(self, requires_grad=False, pretrained=True, num=18)
eval_models/pretrained_networks.py:140
Method__init__
(self)
eval_models/networks_basic.py:95
Method__init__
(self, chn_in, chn_out=1, use_dropout=False)
eval_models/networks_basic.py:106
Method__init__
(self, chn_mid=32, use_sigmoid=True)
eval_models/networks_basic.py:116
Method__init__
(self, chn_mid=32)
eval_models/networks_basic.py:132
Method__init__
(self, use_gpu=True, colorspace='Lab')
eval_models/networks_basic.py:145
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:41
Method__init__
(self, nz, channel, sz=4)
pg_modules/blocks.py:51
Method__init__
(self, in_planes, out_planes, z_dim)
pg_modules/blocks.py:74
Method__init__
(self, in_planes, out_planes, z_dim)
pg_modules/blocks.py:107
Method__init__
(self, ch_in, ch_out)
pg_modules/blocks.py:139
Method__init__
(self, in_channels, out_channels, kernel_size, bias=False)
pg_modules/blocks.py:157
Method__init__
(self, in_planes, out_planes, separable=False)
pg_modules/blocks.py:171
Method__init__
(self, in_planes, out_planes, separable=False)
pg_modules/blocks.py:192
Method__init__
(self, cin, activation, bn)
pg_modules/blocks.py:209
Method__init__
(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True, lowest=False)
pg_modules/blocks.py:219
Method__init__
(self)
pg_modules/blocks.py:252
Method__init__
Init. Args: scale_factor (float): scaling mode (str): interpolation mode
pg_modules/blocks.py:294
Method__init__
(self, nc=None, ndf=None, start_sz=256, end_sz=8, head=None, separable=False, patch=False, c_dim=1000, cmap_di
pg_modules/discriminator.py:70
Method__init__
( self, channels, resolutions, num_discs=4, proj_type=2, # 0 = no pro
pg_modules/discriminator.py:129
Method__init__
( self, diffaug=True, interp224=True, backbone_kwargs={}, **kwargs
pg_modules/discriminator.py:170
Method__init__
(self)
pg_modules/networks_fastgan.py:14
Method__init__
(self, ngf=128, z_dim=256, nc=3, img_resolution=256, lite=False)
pg_modules/networks_fastgan.py:22
Method__init__
(self, ngf=64, z_dim=256, nc=3, img_resolution=256, num_classes=1000, lite=False)
pg_modules/networks_fastgan.py:85
Method__init__
(self, in_features, # Number of input features. out_features, # N
pg_modules/networks_stylegan2.py:89
Method__init__
(self, in_channels, # Number of input channels. out_channels,
pg_modules/networks_stylegan2.py:127
Method__init__
(self, z_dim, # Input latent (Z) dimensionality, 0 = no latent. c_dim,
pg_modules/networks_stylegan2.py:184
Method__init__
(self, in_channels, # Number of input channels. out_channels,
pg_modules/networks_stylegan2.py:266
Method__init__
(self, in_channels, out_channels, w_dim, kernel_size=1, conv_clamp=None, channels_last=False)
pg_modules/networks_stylegan2.py:329
Method__init__
(self, in_channels, # Number of input channels, 0 = first block. ou
pg_modules/networks_stylegan2.py:353
Method__init__
(self, w_dim, # Intermediate latent (W) dimensionality. img_resolution,
pg_modules/networks_stylegan2.py:456
Method__init__
( self, im_res=256, cout=64, expand=True, proj_type=2, # 0 = no proje
pg_modules/projector.py:94
Method__len__
(self)
cp_dataset_test.py:241
Method__len__
(self)
cp_dataset.py:273
Method__setstate__
(self, state)
sync_batchnorm/comm.py:81
Method_check_input_dim
(self, input)
sync_batchnorm/batchnorm.py:249
Method_check_input_dim
(self, input)
sync_batchnorm/batchnorm.py:313
Method_data_parallel_master
Reduce the sum and square-sum, compute the statistics, and broadcast it.
sync_batchnorm/batchnorm.py:92
Functionadd_norm_layer
(layer)
network_generator.py:450
MethodassertTensorClose
(self, x, y)
sync_batchnorm/unittest.py:16
Functioncal_miou
(prediction, target)
utils.py:80
Functionchangearm
(old_label)
utils.py:13
Functionconvert_model
Traverse the input module and its child recursively and replace all instance of torch.nn.modules.batchnorm.BatchNorm*N*d to Synchronized
sync_batchnorm/batchnorm.py:320
Functiondssim
(p0, p1, range=255.)
eval_models/__init__.py:52
Functionembedding
(*args, **kwargs)
pg_modules/blocks.py:19
Methodextra_repr
(self)
pg_modules/networks_stylegan2.py:121
Methodextra_repr
(self)
pg_modules/networks_stylegan2.py:176
Methodextra_repr
(self)
pg_modules/networks_stylegan2.py:260
Methodextra_repr
(self)
pg_modules/networks_stylegan2.py:321
Methodextra_repr
(self)
pg_modules/networks_stylegan2.py:347
Methodextra_repr
(self)
pg_modules/networks_stylegan2.py:450
Methodextra_repr
(self)
pg_modules/networks_stylegan2.py:506
Methodforward
(self, *inputs)
network_generator.py:48
Methodforward
(self, x, mask)
network_generator.py:68
Methodforward
(self, x, seg, misalign_mask=None)
network_generator.py:101
Methodforward
(self, x, seg, misalign_mask=None)
network_generator.py:159
Methodforward
(self, x, seg)
network_generator.py:224
Methodforward
(self, input)
network_generator.py:287
Methodforward
(self, input)
network_generator.py:315
Methodforward
(self, input1, input2, upsample='bilinear')
networks.py:160
Methodforward
(self, x)
networks.py:285
Methodforward
(self, x, y)
networks.py:332
Methodforward
(self, input)
networks.py:415
Methodforward
(self, input)
networks.py:484
Methodforward
(self)
eval_models/base_model.py:18
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