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Functions284 in github.com/SHShim0513/SD-VITON

⨍ Functions 284◇ Types & classes 77
↓ 18 callersFunctionconv2d
(*args, **kwargs)
pg_modules/blocks.py:11
↓ 11 callersMethod__init__
(self, output_size, input_size, which_linear, eps=1e-5, momentum=0.1)
pg_modules/blocks.py:266
↓ 8 callersMethod__init__
(self, gan_mode, target_real_label=1.0, target_fake_label=0.0, tensor=torch.FloatTensor)
network_generator.py:362
↓ 8 callersMethodeval
(self)
pg_modules/discriminator.py:192
↓ 8 callersFunctionsave_checkpoint
(model, save_path)
networks.py:495
↓ 7 callersFunctionNormLayer
(c, mode=None)
pg_modules/blocks.py:27
↓ 7 callersMethod__init__
(self, z_dim, # Input latent (Z) dimensionality. c_dim,
pg_modules/networks_stylegan2.py:515
↓ 6 callersMethod__init__
(self, requires_grad=False)
networks.py:291
↓ 6 callersMethodtrain
(self, mode=True)
pg_modules/discriminator.py:187
↓ 5 callersMethod__init__
(self, pnet_type='vgg', pnet_rand=False, pnet_tune=False, use_dropout=True, spatial=False, version='0.1', lpip
eval_models/networks_basic.py:28
↓ 5 callersFunction_unsqueeze_ft
add new dementions at the front and the tail
sync_batchnorm/batchnorm.py:31
↓ 5 callersFunctionload_checkpoint
(model, checkpoint_path)
networks.py:502
↓ 5 callersMethodloss
(self, input, target_is_real, for_discriminator=True)
network_generator.py:400
↓ 5 callersFunctionmake_grid
(N, iH, iW)
networks.py:254
↓ 4 callersMethodforward
(self, in0, in1, retPerLayer=None)
eval_models/networks_basic.py:152
↓ 4 callersFunctionmake_grid_3d
(N, iH, iW)
networks.py:247
↓ 4 callersMethodprint_network
(self)
network_generator.py:13
↓ 3 callersMethod__init__
(self, requires_grad=False, pretrained=True)
eval_models/pretrained_networks.py:98
↓ 3 callersMethod__init__
(self, nc=None, ndf=None, start_sz=256, end_sz=8, head=None, separable=False, patch=False)
pg_modules/discriminator.py:15
↓ 3 callersMethod__init__
( self, z_dim=256, c_dim=0, w_dim=0, img_resolution=256, img_c
pg_modules/networks_fastgan.py:151
↓ 3 callersMethoddownsample
(self, input)
network_generator.py:310
↓ 3 callersMethodforward
Function computes the distance between image patches in0 and in1 INPUTS in0, in1 - torch.Tensor object of shape Nx3xXxY - image p
eval_models/dist_model.py:109
↓ 3 callersMethodget
(self)
sync_batchnorm/comm.py:32
↓ 3 callersMethodnext_batch
(self)
cp_dataset.py:292
↓ 3 callersMethodnormalize
(self, x)
networks.py:157
↓ 3 callersMethodput
(self, result)
sync_batchnorm/comm.py:26
↓ 3 callersFunctionremove_overlap
(seg_out, warped_cm)
train_condition.py:16
↓ 3 callersMethodsave_network
(self, network, path, network_label, epoch_label)
eval_models/base_model.py:37
↓ 3 callersFunctionset_requires_grad
(nets, requires_grad=False)
network_generator.py:327
↓ 2 callersMethod_check_input_dim
(self, input)
sync_batchnorm/batchnorm.py:186
↓ 2 callersFunction_sum_ft
sum over the first and last dimention
sync_batchnorm/batchnorm.py:26
↓ 2 callersFunctionexecute_replication_callbacks
Execute an replication callback `__data_parallel_replicate__` on each module created by original replication. The callback will be invoked w
sync_batchnorm/replicate.py:27
↓ 2 callersFunctionget_out_channel
(layer)
network_generator.py:445
↓ 2 callersMethodget_target_tensor
(self, input, target_is_real)
network_generator.py:382
↓ 2 callersMethodget_target_tensor
(self, input, target_is_real)
networks.py:356
↓ 2 callersMethodget_zero_tensor
(self, input)
network_generator.py:394
↓ 2 callersFunctionim2tensor
(image, imtype=np.uint8, cent=1., factor=255./2.)
eval_models/__init__.py:111
↓ 2 callersMethodinit_weights
(self, init_type='normal', gain=0.02)
network_generator.py:20
↓ 2 callersMethodinitialize
INPUTS model - ['net-lin'] for linearly calibrated network ['net'] for off-the-shelf network
eval_models/dist_model.py:28
↓ 2 callersFunctionmodulated_conv2d
( x, # Input tensor of shape [batch_size, in_channels, in_height, in_width].
pg_modules/networks_stylegan2.py:27
↓ 2 callersFunctionnormalize_2nd_moment
(x, dim=1, eps=1e-8)
pg_modules/networks_stylegan2.py:22
↓ 2 callersMethodnormalize_region
(self, region, mask)
network_generator.py:58
↓ 2 callersFunctionnormalize_second_moment
(x, dim=1, eps=1e-8)
pg_modules/networks_fastgan.py:9
↓ 2 callersFunctionnp2tensor
(np_obj)
eval_models/__init__.py:66
↓ 2 callersFunctionremove_overlap
(seg_out, warped_cm)
train_generator.py:27
↓ 2 callersMethodreset_parameters
(self)
sync_batchnorm/batchnorm_reimpl.py:43
↓ 2 callersMethodsave
(self, path, label)
eval_models/dist_model.py:195
↓ 2 callersMethodsave
(self, label)
eval_models/base_model.py:33
↓ 2 callersFunctionspatial_average
(in_tens, keepdim=True)
eval_models/networks_basic.py:17
↓ 2 callersFunctionupsample
(in_tens, out_H=64)
eval_models/networks_basic.py:20
↓ 2 callersFunctionvisualize_segmap
(input, multi_channel=True, tensor_out=True, batch=0)
utils.py:49
↓ 1 callersFunctionEvaluation
(opt, pred_list, gt_list)
evaluate.py:28
↓ 1 callersMethod__data_parallel_replicate__
(self, ctx, copy_id)
sync_batchnorm/batchnorm.py:82
↓ 1 callersMethod__init__
(self, opt, dataset)
cp_dataset_test.py:246
↓ 1 callersMethod__init__
(self, opt)
cp_dataset.py:17
↓ 1 callersMethod__init__
Args: master_callback: a callback to be invoked after having collected messages from slave devices.
sync_batchnorm/comm.py:67
↓ 1 callersMethod_compute_mean_std
Compute the mean and standard-deviation with sum and square-sum. This method also maintains the moving average on the master device.
sync_batchnorm/batchnorm.py:115
↓ 1 callersFunction_make_efficientnet
(model)
pg_modules/projector.py:33
↓ 1 callersFunction_make_projector
(im_res, cout, proj_type, expand=False)
pg_modules/projector.py:59
↓ 1 callersFunction_make_scratch_ccm
(scratch, in_channels, cout, expand=False)
pg_modules/projector.py:7
↓ 1 callersFunction_make_scratch_csm
(scratch, in_channels, cout, expand)
pg_modules/projector.py:21
↓ 1 callersMethodbackward_train
(self)
eval_models/dist_model.py:162
↓ 1 callersFunctioncalc_channels
(pretrained, inp_res=224)
pg_modules/projector.py:42
↓ 1 callersMethodclamp_weights
(self)
eval_models/dist_model.py:127
↓ 1 callersMethodcompute_accuracy
d0, d1 are Variables, judge is a Tensor
eval_models/dist_model.py:165
↓ 1 callersMethodcompute_latent_vector_size
(self, opt)
network_generator.py:210
↓ 1 callersFunctionconvTranspose2d
(*args, **kwargs)
pg_modules/blocks.py:15
↓ 1 callersFunctioncreate_network
(cls, opt)
utils.py:112
↓ 1 callersFunctioncross_entropy2d
(input, target, weight=None, size_average=True)
utils.py:29
↓ 1 callersFunctiondefine_D
(input_nc, ndf=64, n_layers_D=3, norm='instance', use_sigmoid=False, num_D=2, getIntermFeat=False, gpu_ids=[],
networks.py:527
↓ 1 callersMethodforward
(self, X)
networks.py:313
↓ 1 callersMethodforward
Pred and target are Variables. If normalize is True, assumes the images are between [0,1] and then scales them between [-1,+1]
eval_models/__init__.py:26
↓ 1 callersMethodforward_train
(self)
eval_models/dist_model.py:148
↓ 1 callersMethodget_agnostic
(self, im, im_parse, pose_data)
cp_dataset_test.py:48
↓ 1 callersMethodget_agnostic
(self, im, im_parse, pose_data)
cp_dataset.py:56
↓ 1 callersFunctionget_nonspade_norm_layer
(norm_type='instance')
network_generator.py:444
↓ 1 callersFunctionget_norm_layer
(norm_type='instance')
networks.py:518
↓ 1 callersFunctionget_opt
()
train_condition.py:23
↓ 1 callersFunctionget_opt
()
test_generator.py:28
↓ 1 callersFunctionget_opt
()
evaluate.py:17
↓ 1 callersFunctionget_opt
()
train_generator.py:34
↓ 1 callersFunctionload_checkpoint_G
(model, checkpoint_path)
test_generator.py:82
↓ 1 callersMethodloss
(self, input, target_is_real, for_discriminator=True)
network_generator.py:345
↓ 1 callersFunctionmain
()
train_condition.py:278
↓ 1 callersFunctionmain
()
test_generator.py:229
↓ 1 callersFunctionmain
()
evaluate.py:116
↓ 1 callersFunctionmain
()
train_generator.py:578
↓ 1 callersMethodmake_grid
(self, N, iH, iW)
cp_dataset.py:50
↓ 1 callersMethodname
(self)
eval_models/dist_model.py:25
↓ 1 callersFunctionndim_tensor2im
(image_tensor, imtype=np.uint8, batch=0)
utils.py:44
↓ 1 callersFunctionpred_to_onehot
(prediction)
utils.py:72
↓ 1 callersMethodregister_slave
Register an slave device. Args: identifier: an identifier, usually is the device id. Returns: a `SlavePipe` obj
sync_batchnorm/comm.py:84
↓ 1 callersFunctionremove_overlap
(seg_out, warped_cm)
test_generator.py:21
↓ 1 callersMethodreset_running_stats
(self)
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
sync_batchnorm/comm.py:102
↓ 1 callersMethodrun_slave
(self, msg)
sync_batchnorm/comm.py:49
↓ 1 callersFunctionsave_images
(img_tensors, img_names, save_dir)
utils.py:93
↓ 1 callersMethodshortcut
(self, x, seg, misalign_mask)
network_generator.py:153
↓ 1 callersMethodsingleD_forward
(self, model, input)
networks.py:406
↓ 1 callersFunctiontensor2im
(image_tensor, imtype=np.uint8, cent=1., factor=255./2.)
eval_models/__init__.py:106
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