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Functions258 in github.com/barisgecer/TBGAN

⨍ Functions 258◇ Types & classes 20
↓ 1 callersFunctionfix_obj
(fp)
util_scripts.py:431
↓ 1 callersMethodflush
(self)
misc.py:131
↓ 1 callersFunctionfrom_UV_2_3D
(uv, uv_layout='oval', topology='full', plot=False)
UV_manipulation_2.py:61
↓ 1 callersFunctiongenerate_laplacian_pyramid
(minibatch, num_levels)
metrics/sliced_wasserstein.py:83
↓ 1 callersFunctionget_descriptors_for_minibatch
(minibatch, nhood_size, nhoods_per_image)
metrics/sliced_wasserstein.py:13
↓ 1 callersFunctionget_inception_score
(images, splits=10)
metrics/inception_score.py:41
↓ 1 callersMethodget_metric_names
(self)
metrics/sliced_wasserstein.py:110
↓ 1 callersMethodget_metric_names
(self)
metrics/ms_ssim.py:181
↓ 1 callersMethodget_minibatch_tf
(self)
dataset.py:197
↓ 1 callersMethodget_random_labels_tf
(self, minibatch_size)
dataset.py:144
↓ 1 callersMethodget_random_labels_tf
(self, minibatch_size)
dataset.py:211
↓ 1 callersMethodget_result
(self, func)
dataset_tool.py:194
↓ 1 callersFunctionhe_std
(gain, w)
legacy.py:64
↓ 1 callersFunctionimport_uv_info
(instance, res, uv_layout='oval', topology='full')
UV_manipulation_2.py:35
↓ 1 callersFunctionlerp
(a, b, t)
tfutil.py:39
↓ 1 callersMethodlist_layers
(self)
tfutil.py:723
↓ 1 callersFunctionlist_network_pkls
(run_id_or_result_subdir, include_final=True)
misc.py:218
↓ 1 callersFunctionload_mean
()
UV_manipulation_2.py:21
↓ 1 callersFunctionload_pkl
(filename)
misc.py:27
↓ 1 callersFunctionlocate_network_pkl
(run_id_or_result_subdir_or_network_pkl, snapshot=None)
misc.py:227
↓ 1 callersFunctionminibatch_stddev_layer
(x, group_size=4)
networks.py:127
↓ 1 callersFunctionmsssim
Return the MS-SSIM score between `img1` and `img2`. This function implements Multi-Scale Structural Similarity (MS-SSIM) Image Quality Assess
metrics/ms_ssim.py:113
↓ 1 callersMethodparse_tfrecord_np
(self, record)
dataset.py:164
↓ 1 callersFunctionprocess_func
(idx)
dataset_tool.py:548
↓ 1 callersMethodprocess_items_concurrently
(self, item_iterator, process_func=lambda x: x, pre_func=lambda x: x, post_func=lambda x: x, max_items_in_flig
dataset_tool.py:211
↓ 1 callersFunctionprocess_reals
(x, lod, mirror_augment, drange_data, drange_net)
train.py:56
↓ 1 callersFunctionprocess_reals
(x, lod, mirror_augment, drange_data, drange_net)
test.py:56
↓ 1 callersFunctionpyr_down
(minibatch)
metrics/sliced_wasserstein.py:72
↓ 1 callersMethodset_log_file
(self, filename, mode='wt')
misc.py:103
↓ 1 callersFunctionset_output_log_file
(filename, mode='wt')
misc.py:144
↓ 1 callersFunctionsetup_snapshot_image_grid
(G, training_set, size = '1080p', # '1080p' = to be viewed on 1080p display, '4k' = to be viewed
train.py:22
↓ 1 callersFunctionsetup_snapshot_image_grid
(G, training_set, size = '1080p', # '1080p' = to be viewed on 1080p display, '4k' = to be viewed o
test.py:22
↓ 1 callersFunctionsetup_text_label
(text, font='Calibri', fontsize=32, padding=6, glow_size=2.0, glow_coef=3.0, glow_exp=2.0, cache_size=100)
misc.py:329
↓ 1 callersFunctionsliced_wasserstein
(A, B, dir_repeats, dirs_per_repeat)
metrics/sliced_wasserstein.py:38
↓ 1 callersFunctionwscale
(gain, w)
legacy.py:65
FunctionD_paper
( images_in, # Input: Images [minibatch, channel, height, width]. num_channel
networks.py:278
FunctionD_wgangp_acgan
(G, D, opt, training_set, minibatch_size, reals, labels, wgan_lambda = 10.0, # Weight for the grad
loss.py:43
FunctionG_paper
( latents_in, # First input: Latent vectors [minibatch, latent_size]. labels_i
networks.py:144
FunctionG_wgan_acgan
(G, D, opt, training_set, minibatch_size, cond_weight = 1.0)
loss.py:24
Method__delattr__
(self, name)
config.py:17
Method__delattr__
(self, name)
config_test.py:17
Method__enter__
(self)
dataset_tool.py:144
Method__exit__
(self, *args)
dataset_tool.py:147
Method__exit__
(self, *excinfo)
dataset_tool.py:208
Method__getattr__
(self, name)
config.py:15
Method__getattr__
(self, name)
config_test.py:15
Method__getstate__
(self)
tfutil.py:538
Method__init__
(self, *args, **kwargs)
config.py:14
Method__init__
(self, *args, **kwargs)
config_test.py:14
Method__init__
(self, *args, **kwargs)
legacy.py:20
Method__init__
( self, cur_nimg, training_set, lod_initial_resolution = 4, # Imag
train.py:87
Method__init__
( self, cur_nimg, training_set, lod_initial_resolution = 4, # Image re
test.py:87
Method__init__
(self)
misc.py:99
Method__init__
(self, child_streams, autoflush=False)
misc.py:121
Method__init__
(self, tfrecord_dir, expected_images, print_progress=True, progress_interval=10)
dataset_tool.py:33
Method__init__
(self)
dataset_tool.py:153
Method__init__
(self, task_queue)
dataset_tool.py:160
Method__init__
(self, tfrecord_dir, # Directory containing a collection of tfrecords files. res
dataset.py:22
Method__init__
(self, resolution=1024, num_channels=3, dtype='uint8', dynamic_range=[0,255], label_size=0, label_dtype='float
dataset.py:174
Method__init__
( self, name = 'Train', tf_optimizer = 'tf.train.AdamOptimizer',
tfutil.py:245
Method__init__
(self, name=None, # Network name. Used to select TensorFlow name and variable scopes.
tfutil.py:415
Method__init__
(self, num_images, image_shape, image_dtype, minibatch_size)
metrics/sliced_wasserstein.py:99
Method__init__
(self, num_images, image_shape, image_dtype, minibatch_size)
metrics/inception_score.py:122
Method__init__
(self, num_images, image_shape, image_dtype, minibatch_size)
metrics/ms_ssim.py:177
Method__init__
(self, num_images, image_shape, image_dtype, minibatch_size)
metrics/frechet_inception_distance.py:250
Method__setattr__
(self, name, value)
config.py:16
Method__setattr__
(self, name, value)
config_test.py:16
Method__setstate__
(self, state)
tfutil.py:548
Function_progress
(count, block_size, total_size)
metrics/inception_score.py:79
Functionalignment
(mesh)
UV_manipulation_2.py:25
Functionapply_mirror_augment
(minibatch)
misc.py:305
Methodbegin
(self, mode)
metrics/sliced_wasserstein.py:116
Methodbegin
(self, mode)
metrics/inception_score.py:134
Methodbegin
(self, mode)
metrics/frechet_inception_distance.py:263
Functioncall_func_by_name
(*args, func=None, **kwargs)
tfutil.py:232
Functioncompare
(tfrecord_dir_a, tfrecord_dir_b, ignore_labels)
dataset_tool.py:292
Functionconcat_image
(im1, im2)
myutil.py:42
Methodconvert
(self, name=None, func=None, **static_kwargs)
tfutil.py:599
Methodcopy_trainables_from
(self, src_net)
tfutil.py:593
Functioncreate_celeba
(tfrecord_dir, celeba_dir, cx=89, cy=121)
dataset_tool.py:485
Functioncreate_celebahq
(tfrecord_dir, celeba_dir, delta_dir, num_threads=4, num_tasks=100)
dataset_tool.py:504
Functioncreate_cifar10
(tfrecord_dir, cifar10_dir)
dataset_tool.py:376
Functioncreate_cifar100
(tfrecord_dir, cifar100_dir)
dataset_tool.py:403
Functioncreate_from_hdf5
(tfrecord_dir, hdf5_filename, shuffle)
dataset_tool.py:829
Functioncreate_from_images
(tfrecord_dir, image_dir, shuffle)
dataset_tool.py:648
Functioncreate_from_pkl
(tfrecord_dir, image_dir, shuffle)
dataset_tool.py:682
Functioncreate_from_pkl_img
(tfrecord_dir, image_dir, pickle_dir, shuffle)
dataset_tool.py:713
Functioncreate_from_pkl_img_norm
(tfrecord_dir, mein3d_image_dir, mein3d_pickle_dir, mein3d_normal_dir, shuffle)
dataset_tool.py:746
Functioncreate_lsun
(tfrecord_dir, lmdb_dir, resolution=256, max_images=None)
dataset_tool.py:452
Functioncreate_mnist
(tfrecord_dir, mnist_dir)
dataset_tool.py:335
Functioncreate_mnistrgb
(tfrecord_dir, mnist_dir, num_images=1000000, random_seed=123)
dataset_tool.py:359
Functioncreate_result_subdir
(result_dir, desc)
misc.py:151
Functioncreate_svhn
(tfrecord_dir, svhn_dir)
dataset_tool.py:425
Functioncrop_im
(img)
myutil.py:6
Functioncrop_im_377
(img_512)
myutil.py:26
Functioncrop_im_512
(img_377)
myutil.py:12
Functiondisplay
(tfrecord_dir)
dataset_tool.py:239
Functiondownscale_minibatch
(minibatch, lod)
metrics/sliced_wasserstein.py:55
Functiondraw_text_label
(img, text, x, y, alignx=0.5, aligny=0.5, color=255, opacity=1.0, glow_opacity=1.0, **kwargs)
misc.py:316
Methodend
(self, mode)
metrics/sliced_wasserstein.py:125
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