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Functions334 in github.com/crazy-bot/VOGUE-Try-On-by-StyleGAN-Interpolation-Optimization

↓ 44 callersFunctionkwarg
(tf_name, default=None, none=None)
legacy.py:116
↓ 26 callersMethodsave
(self, pkl_file)
metrics/metric_utils.py:121
↓ 25 callersMethodmean
r"""Returns the mean of the scalars that were accumulated for the given statistic between the last two calls to `update()`, or NaN if
torch_utils/training_stats.py:188
↓ 23 callersMethodappend
(self, x)
metrics/metric_utils.py:78
↓ 22 callersMethodload
(pkl_file)
metrics/metric_utils.py:126
↓ 13 callersMethod__init__
(self, in_channels, out_channels)
training/networks.py:270
↓ 12 callersMethodupdate
(self, cur_items)
metrics/metric_utils.py:151
↓ 8 callersFunctionerror
(msg)
dataset_tool.py:28
↓ 7 callersFunctionmatrix
(*rows, device=None)
training/augment.py:43
↓ 7 callersMethodsub
(self, tag=None, num_items=None, flush_interval=1000, rel_lo=0, rel_hi=1)
metrics/metric_utils.py:166
↓ 6 callersFunction_conv2d_wrapper
Wrapper for the underlying `conv2d()` and `conv_transpose2d()` implementations.
torch_utils/ops/conv2d_resample.py:29
↓ 6 callersFunction_parse_padding
(padding)
torch_utils/ops/upfirdn2d.py:46
↓ 6 callersFunction_parse_scaling
(scaling)
torch_utils/ops/upfirdn2d.py:37
↓ 6 callersMethodbackward
(ctx, dy)
torch_utils/ops/bias_act.py:161
↓ 6 callersMethodupdate
r"""Copies current values of the internal counters to the user-visible state and resets them for the next round. If `keep_previous=Tr
torch_utils/training_stats.py:147
↓ 5 callersFunction_get_filter_size
(f)
torch_utils/ops/upfirdn2d.py:57
↓ 5 callersFunctioniterate_images
()
dataset_tool.py:68
↓ 5 callersFunctionmaybe_min
(a: int, b: Optional[int])
dataset_tool.py:34
↓ 5 callersFunctionscale2d_inv
(sx, sy, **kwargs)
training/augment.py:103
↓ 4 callersFunction_conv2d_gradfix
(transpose, weight_shape, stride, padding, output_padding, dilation, groups)
torch_utils/ops/conv2d_gradfix.py:68
↓ 4 callersMethod_get_raw_labels
(self)
training/dataset.py:51
↓ 4 callersFunction_tuple_of_ints
(xs, ndim)
torch_utils/ops/conv2d_gradfix.py:58
↓ 4 callersMethodconvert
(self, value, param, ctx)
train.py:394
↓ 4 callersMethodflush
Flush written text to both stdout and a file, if open.
dnnlib/util.py:93
↓ 4 callersMethodget_all
(self)
metrics/metric_utils.py:107
↓ 4 callersFunctionget_binary_mask
(img, type='garment')
Interpolation/identity_garment_loss_sup.py:106
↓ 4 callersFunctionhas_same_layout
torch_utils/ops/bias_act.cpp:16
↓ 4 callersFunctionperceptual_distance
(img1, img2)
Interpolation/identity_garment_loss_sup.py:90
↓ 4 callersMethodwrite
Write text to stdout (and a file) and optionally flush.
dnnlib/util.py:78
↓ 3 callersMethod_file_ext
(fname)
training/dataset.py:184
↓ 3 callersMethod_get_delta
r"""Returns the raw moments that were accumulated for the given statistic between the last two calls to `update()`, or zero if no scal
torch_utils/training_stats.py:170
↓ 3 callersMethod_load_raw_image
(self, fname)
training/dataset.py:289
↓ 3 callersFunction_unbroadcast
(x, shape)
torch_utils/ops/fma.py:49
↓ 3 callersMethodappend_torch
(self, x, num_gpus=1, rank=0)
metrics/metric_utils.py:95
↓ 3 callersFunctioncords_to_map
(keypoints, img_size = (512,512), std = 6)
Interpolation/util_latent.py:34
↓ 3 callersFunctionfile_ext
(name: Union[str, Path])
dataset_tool.py:41
↓ 3 callersMethodget_label
(self, idx)
training/dataset.py:95
↓ 3 callersFunctionget_obj_from_module
Traverses the object name and returns the last (rightmost) python object.
dnnlib/util.py:263
↓ 3 callersFunctionnamed_params_and_buffers
(module)
torch_utils/misc.py:156
↓ 3 callersFunctionrotate2d_inv
(theta, **kwargs)
training/augment.py:106
↓ 3 callersMethodrun_D
(self, img, sync)
training/loss.py:52
↓ 3 callersMethodrun_G
(self, z, pose, sync)
training/loss.py:39
↓ 3 callersFunctionsave_image_grid
(img, fname, drange, grid_size)
training/training_loop.py:69
↓ 3 callersFunctionscale2d
(sx, sy, **kwargs)
training/augment.py:68
↓ 3 callersFunctiontranslate2d
(tx, ty, **kwargs)
training/augment.py:53
↓ 3 callersFunctiontranslate2d_inv
(tx, ty, **kwargs)
training/augment.py:100
↓ 3 callersFunctionupfirdn2d
r"""Pad, upsample, filter, and downsample a batch of 2D images. Performs the following sequence of operations for each channel: 1. Upsample
torch_utils/ops/upfirdn2d.py:120
↓ 2 callersMethod__init__
(self, name, # Name of the dataset. raw_shape, # Shape of the r
training/dataset.py:25
↓ 2 callersFunction_collect_tf_params
(tf_net)
legacy.py:75
↓ 2 callersFunction_get_weight_shape
(w)
torch_utils/ops/conv2d_resample.py:21
↓ 2 callersMethod_get_zipfile
(self)
training/dataset.py:187
↓ 2 callersMethod_open_file
(self, fname)
training/dataset.py:193
↓ 2 callersFunction_populate_module_params
(module, *patterns)
legacy.py:88
↓ 2 callersFunction_should_use_custom_op
(input)
torch_utils/ops/conv2d_gradfix.py:47
↓ 2 callersFunction_upfirdn2d_cuda
Fast CUDA implementation of `upfirdn2d()` using custom ops.
torch_utils/ops/upfirdn2d.py:214
↓ 2 callersFunctioncalc_output_padding
(input_shape, output_shape)
torch_utils/ops/conv2d_gradfix.py:95
↓ 2 callersMethodclose
(self)
training/dataset.py:64
↓ 2 callersMethodclose
Flush, close possible files, and remove stdout/stderr mirroring.
dnnlib/util.py:100
↓ 2 callersFunctioncompute_distances
(row_features, col_features, num_gpus, rank, col_batch_size)
metrics/precision_recall.py:19
↓ 2 callersFunctionconvert_tf_generator
(tf_G)
legacy.py:109
↓ 2 callersMethodcords_to_map
(self, keypoints, img_size = (512,512), std = 6)
training/dataset.py:307
↓ 2 callersMethodget_all_torch
(self)
metrics/metric_utils.py:111
↓ 2 callersFunctionget_feature_detector
(url, device=torch.device('cpu'), num_gpus=1, rank=0, verbose=False)
metrics/metric_utils.py:40
↓ 2 callersFunctionget_m_i
(m)
Interpolation/util_latent.py:115
↓ 2 callersMethodget_mean_cov
(self)
metrics/metric_utils.py:114
↓ 2 callersFunctionget_module_from_obj_name
Searches for the underlying module behind the name to some python object. Returns the module and the object name (original name with module part r
dnnlib/util.py:222
↓ 2 callersFunctionis_image_ext
(fname: Union[str, Path])
dataset_tool.py:46
↓ 2 callersFunctionis_persistent
r"""Test whether the given object or class is persistent, i.e., whether it will save its source code when pickled.
torch_utils/persistence.py:134
↓ 2 callersFunctionis_valid_metric
(metric)
metrics/metric_main.py:31
↓ 2 callersFunctionloadData
(jsonpath, latent_path)
style_mixing_vogue.py:111
↓ 2 callersFunctionloadData
(img_path, mask_path, jsonpath, latent_path)
Interpolation/util_latent.py:47
↓ 2 callersFunctionlogprint
(*args)
projector_vogue.py:47
↓ 2 callersFunctionmodulated_conv2d
( x, # Input tensor of shape [batch_size, in_channels, in_height, in_width].
training/networks.py:27
↓ 2 callersMethodnames
r"""Returns the names of all statistics broadcasted so far that match the regular expression specified at construction time.
torch_utils/training_stats.py:141
↓ 2 callersFunctionnan_to_num
(input, nan=0.0, posinf=None, neginf=None, *, out=None)
torch_utils/misc.py:49
↓ 2 callersFunctionslerp
(a, b, t)
metrics/perceptual_path_length.py:23
↓ 2 callersMethodstd
r"""Returns the standard deviation of the scalars that were accumulated for the given statistic between the last two calls to `update(
torch_utils/training_stats.py:198
↓ 1 callersMethod__getstate__
(self)
training/dataset.py:73
↓ 1 callersMethod__init__
(self, G, person, garment )
Interpolation/main_latent.py:29
↓ 1 callersMethod__reduce__
(self)
torch_utils/persistence.py:118
↓ 1 callersFunction_bias_act_cuda
Fast CUDA implementation of `bias_act()` using custom ops.
torch_utils/ops/bias_act.py:129
↓ 1 callersFunction_bias_act_ref
Slow reference implementation of `bias_act()` using standard TensorFlow ops.
torch_utils/ops/bias_act.py:94
↓ 1 callersFunction_check_pickleable
r"""Check that the given object is pickleable, raising an exception if it is not. This function is expected to be considerably more efficient
torch_utils/persistence.py:231
↓ 1 callersFunction_find_compiler_bindir
()
torch_utils/custom_ops.py:28
↓ 1 callersFunction_init
()
torch_utils/ops/bias_act.py:41
↓ 1 callersFunction_init
()
torch_utils/ops/upfirdn2d.py:26
↓ 1 callersMethod_load_raw_image
(self, raw_idx)
training/dataset.py:67
↓ 1 callersMethod_load_raw_image
(self, raw_idx)
training/dataset.py:210
↓ 1 callersMethod_load_raw_labels
(self)
training/dataset.py:70
↓ 1 callersFunction_module_to_src
r"""Query the source code of a given Python module.
torch_utils/persistence.py:206
↓ 1 callersFunction_should_use_custom_op
()
torch_utils/ops/grid_sample_gradfix.py:34
↓ 1 callersFunction_src_to_module
r"""Get or create a Python module for the given source code.
torch_utils/persistence.py:216
↓ 1 callersFunction_sync
r"""Synchronize the global cumulative counters across devices and processes. Called internally by `Collector.update()`.
torch_utils/training_stats.py:234
↓ 1 callersFunction_upfirdn2d_ref
Slow reference implementation of `upfirdn2d()` using standard PyTorch ops.
torch_utils/ops/upfirdn2d.py:169
↓ 1 callersMethodaccumulate_gradients
(self, phase, real_img, gen_z, gen_pose, sync, gain)
training/loss.py:18
↓ 1 callersFunctionadd_face_to_try_on
(try_on, person_img, person_mask, try_on_mask)
Interpolation/main_latent.py:143
↓ 1 callersFunctionadd_face_to_try_on
(try_on, person_mask, face_img)
Interpolation/add_face.py:4
↓ 1 callersMethodas_dict
r"""Returns the averages accumulated between the last two calls to `update()` as an `dnnlib.EasyDict`. The contents are as follows:
torch_utils/training_stats.py:212
↓ 1 callersFunctioncalc_metrics
Calculate quality metrics for previous training run or pretrained network pickle. Examples: \b # Previous training run: look up options
calc_metrics.py:96
↓ 1 callersFunctioncall_func_by_name
Finds the python object with the given name and calls it as a function.
dnnlib/util.py:279
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