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Functions451 in github.com/apple/ml-facelit

FunctionPYBIND11_MODULE
facelit/torch_utils/ops/bias_act.cpp:98
FunctionPYBIND11_MODULE
facelit/torch_utils/ops/filtered_lrelu.cpp:298
FunctionPYBIND11_MODULE
facelit/torch_utils/ops/upfirdn2d.cpp:106
Method__del__
(self)
facelit/training/dataset.py:90
Method__delattr__
(self, name: str)
facelit/dnnlib/util.py:54
Method__enter__
(self)
facelit/dnnlib/util.py:74
Method__exit__
(self, exc_type: Any, exc_value: Any, traceback: Any)
facelit/dnnlib/util.py:77
Method__getattr__
(self, name: str)
facelit/dnnlib/util.py:45
Method__getitem__
(self, x)
facelit/light_utils.py:16
Method__getitem__
(self, x)
facelit/deca_utils.py:17
Method__getitem__
r"""Convenience getter. `collector[name]` is a synonym for `collector.mean(name)`.
facelit/torch_utils/training_stats.py:228
Method__getitem__
(self, idx)
facelit/training/dataset.py:99
Method__getstate__
(self)
facelit/training/dataset.py:225
Method__init__
(self, file_path, n_samples=10)
facelit/light_utils.py:9
Method__init__
(self, file_path, n_samples=10)
facelit/deca_utils.py:10
Method__init__
(self, dataset, rank=0, num_replicas=1, shuffle=True, seed=0, window_size=0.5)
facelit/torch_utils/misc.py:114
Method__init__
(self, *args, **kwargs)
facelit/torch_utils/persistence.py:105
Method__init__
(self, regex='.*', keep_previous=True)
facelit/torch_utils/training_stats.py:135
Method__init__
(self, G, G_kwargs, epsilon, space, sampling, crop, vgg16)
facelit/metrics/perceptual_path_length.py:38
Method__init__
(self, G=None, G_kwargs={}, dataset_kwargs={}, num_gpus=1, rank=0, device=None, progress=None, cache=True)
facelit/metrics/metric_utils.py:26
Method__init__
(self, capture_all=False, capture_mean_cov=False, max_items=None)
facelit/metrics/metric_utils.py:74
Method__init__
(self, tag=None, num_items=None, flush_interval=1000, verbose=False, progress_fn=None, pfn_lo=0, pfn_hi=1000,
facelit/metrics/metric_utils.py:154
Method__init__
(self, device, G, D, augment_pipe=None, r1_gamma=10, style_mixing_prob=0, pl_weight=0, pl_batch_shrink=2, pl_d
facelit/training/loss.py:29
Method__init__
(self, in_features, # Number of input features. out_features, # N
facelit/training/networks_stylegan3.py:72
Method__init__
(self, z_dim, # Input latent (Z) dimensionality. c_dim,
facelit/training/networks_stylegan3.py:112
Method__init__
(self, w_dim, # Intermediate latent (W) dimensionality. channels, # Number of o
facelit/training/networks_stylegan3.py:172
Method__init__
(self, w_dim, # Intermediate latent (W) dimensionality. is_torgb,
facelit/training/networks_stylegan3.py:256
Method__init__
(self, w_dim, # Intermediate latent (W) dimensionality. img_resolutio
facelit/training/networks_stylegan3.py:402
Method__init__
(self, path, # Path to directory or zip. resolution = None, # Ensure sp
facelit/training/dataset.py:172
Method__init__
(self, channels, img_resolution, sr_num_fp16_res, sr_antialias, num_fp16_res=4, conv_clamp=Non
facelit/training/superresolution.py:30
Method__init__
(self, channels, img_resolution, sr_num_fp16_res, sr_antialias, num_fp16_res=4, conv_clamp=Non
facelit/training/superresolution.py:63
Method__init__
(self, channels, img_resolution, sr_num_fp16_res, sr_antialias, num_fp16_res=4, conv_clamp=Non
facelit/training/superresolution.py:95
Method__init__
(self, channels, img_resolution, sr_num_fp16_res, num_fp16_res=4, conv_clamp=None, channel_bas
facelit/training/superresolution.py:128
Method__init__
(self, channels, img_resolution, sr_num_fp16_res, sr_antialias, num_fp16_res=4, conv_clamp=Non
facelit/training/superresolution.py:265
Method__init__
(self, in_features, # Number of input features. out_features, # N
facelit/training/networks_stylegan2.py:97
Method__init__
(self, in_channels, # Number of input channels. out_channels,
facelit/training/networks_stylegan2.py:136
Method__init__
(self, z_dim, # Input latent (Z) dimensionality, 0 = no latent. c_dim,
facelit/training/networks_stylegan2.py:194
Method__init__
(self, in_channels, # Number of input channels. out_channels,
facelit/training/networks_stylegan2.py:277
Method__init__
(self, in_channels, out_channels, w_dim, kernel_size=1, conv_clamp=None, channels_last=False)
facelit/training/networks_stylegan2.py:341
Method__init__
(self, in_channels, # Number of input channels, 0 = first block. ou
facelit/training/networks_stylegan2.py:366
Method__init__
(self, w_dim, # Intermediate latent (W) dimensionality. img_resolution,
facelit/training/networks_stylegan2.py:470
Method__init__
(self, in_channels, # Number of input channels, 0 = first block. tmp_ch
facelit/training/networks_stylegan2.py:558
Method__init__
(self, group_size, num_channels=1)
facelit/training/networks_stylegan2.py:647
Method__init__
(self, in_channels, # Number of input channels. cmap_dim,
facelit/training/networks_stylegan2.py:676
Method__init__
(self, c_dim, # Conditioning label (C) dimensionality. img_resolution
facelit/training/networks_stylegan2.py:737
Method__init__
(self, z_dim, # Input latent (Z) dimensionality. c_dim,
facelit/training/triplane.py:22
Method__init__
(self, n_features, options)
facelit/training/triplane.py:205
Method__init__
(self, n_features, options)
facelit/training/triplane.py:232
Method__init__
(self, xflip=0, rotate90=0, xint=0, xint_max=0.125, scale=0, rotate=0, aniso=0, xfrac=0, scale
facelit/training/augment.py:125
Method__init__
(self, c_dim, # Conditioning label (C) dimensionality. img_resolution
facelit/training/dual_discriminator.py:22
Method__init__
(self, c_dim, # Conditioning label (C) dimensionality. img_resolution
facelit/training/dual_discriminator.py:180
Method__init__
(self, c_dim, # Conditioning label (C) dimensionality. img_resolution
facelit/training/dual_discriminator.py:252
Method__init__
(self, light_mode='none')
facelit/training/volumetric_rendering/renderer.py:121
Method__init__
(self)
facelit/training/volumetric_rendering/ray_sampler.py:19
Method__init__
(self)
facelit/training/volumetric_rendering/ray_marcher.py:21
Method__init__
(self, file_name: str = None, file_mode: str = "w", should_flush: bool = True)
facelit/dnnlib/util.py:61
Method__iter__
(self)
facelit/torch_utils/misc.py:127
Method__len__
(self)
facelit/light_utils.py:58
Method__len__
(self)
facelit/deca_utils.py:39
Method__len__
(self)
facelit/training/dataset.py:96
Method__setattr__
(self, name: str, value: Any)
facelit/dnnlib/util.py:51
Method_load_raw_labels
labels are in the form of [R|t, I]
facelit/training/dataset.py:240
Function_reconstruct_persistent_obj
r"""Hook that is called internally by the `pickle` module to unpickle a persistent object.
facelit/torch_utils/persistence.py:181
Methodaccumulate_gradients
(self, phase, real_img, real_c, gen_z, gen_c, gain, cur_nimg)
facelit/training/loss.py:91
Functionask_yes_no
Ask the user the question until the user inputs a valid answer.
facelit/dnnlib/util.py:169
Functionassert_shape
(tensor, ref_shape)
facelit/torch_utils/misc.py:84
Methodbackward
(ctx, dy)
facelit/torch_utils/ops/filtered_lrelu.py:241
Methodbackward
(ctx, grad_output)
facelit/torch_utils/ops/grid_sample_gradfix.py:50
Methodbackward
(ctx, grad2_grad_input, grad2_grad_grid)
facelit/torch_utils/ops/grid_sample_gradfix.py:66
Methodbackward
(ctx, d_dx)
facelit/torch_utils/ops/bias_act.py:188
Methodbackward
(ctx, dy)
facelit/torch_utils/ops/upfirdn2d.py:253
Methodbackward
(ctx, dout)
facelit/torch_utils/ops/fma.py:31
Methodbackward
(ctx, grad_output)
facelit/torch_utils/ops/conv2d_gradfix.py:132
Methodbackward
(ctx, grad2_grad_weight)
facelit/torch_utils/ops/conv2d_gradfix.py:177
Functionbias_act
r"""Fused bias and activation function. Adds bias `b` to activation tensor `x`, evaluates activation function `act`, and scales the result by
facelit/torch_utils/ops/bias_act.py:54
Functionbias_act
facelit/torch_utils/ops/bias_act.cpp:36
Functioncalc_metric
(metric, **kwargs)
facelit/metrics/metric_main.py:44
Functioncenter_crop
(width, height, img)
facelit/dataset_tool.py:233
Functioncenter_crop_wide
(width, height, img)
facelit/dataset_tool.py:240
Functioncheck_ddp_consistency
(module, ignore_regex=None)
facelit/torch_utils/misc.py:182
Methodclose
(self)
facelit/training/dataset.py:218
Functioncompute_equivariance_metrics
(opts, num_samples, batch_size, translate_max=0.125, rotate_max=1, compute_eqt_int=False, compute_eqt_frac=Fal
facelit/metrics/equivariance.py:194
Functioncompute_feature_stats_for_dataset
(opts, detector_url, detector_kwargs, rel_lo=0, rel_hi=1, batch_size=64, data_loader_kwargs=None, max_items=No
facelit/metrics/metric_utils.py:198
Functioncompute_feature_stats_for_generator
(opts, detector_url, detector_kwargs, rel_lo=0, rel_hi=1, batch_size=64, batch_gen=None, **stats_kwargs)
facelit/metrics/metric_utils.py:251
Functioncompute_fid
(opts, max_real, num_gen)
facelit/metrics/frechet_inception_distance.py:22
Functioncompute_is
(opts, num_gen, num_splits)
facelit/metrics/inception_score.py:20
Functioncompute_kid
(opts, max_real, num_gen, num_subsets, max_subset_size)
facelit/metrics/kernel_inception_distance.py:20
Functioncompute_ppl
(opts, num_samples, epsilon, space, sampling, crop, batch_size)
facelit/metrics/perceptual_path_length.py:96
Functioncompute_pr
(opts, max_real, num_gen, nhood_size, row_batch_size, col_batch_size)
facelit/metrics/precision_recall.py:38
Functionconstant
(value, shape=None, dtype=None, device=None, memory_format=None)
facelit/torch_utils/misc.py:24
Functionconstruct_class_by_name
Finds the python class with the given name and constructs it with the given arguments.
facelit/dnnlib/util.py:303
Functionconv2d
(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1)
facelit/torch_utils/ops/conv2d_gradfix.py:37
Functionconv2d_resample
r"""2D convolution with optional up/downsampling. Padding is performed only once at the beginning, not between the operations. Args:
facelit/torch_utils/ops/conv2d_resample.py:48
Functionconv_transpose2d
(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1)
facelit/torch_utils/ops/conv2d_gradfix.py:42
Functioncopy_files_and_create_dirs
Takes in a list of tuples of (src, dst) paths and copies files. Will create all necessary directories.
facelit/dnnlib/util.py:364
Functioncopy_params_and_buffers
(src_module, dst_module, require_all=False)
facelit/torch_utils/misc.py:157
Functionddp_sync
(module, sync)
facelit/torch_utils/misc.py:171
Functiondecorator
(*args, **kwargs)
facelit/torch_utils/misc.py:103
Functiondownsample2d
r"""Downsample a batch of 2D images using the given 2D FIR filter. By default, the result is padded so that its shape is a fraction of the input.
facelit/torch_utils/ops/upfirdn2d.py:354
Functioneqr50k
(opts)
facelit/metrics/metric_main.py:123
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