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Functions451 in github.com/apple/ml-facelit

Functioneqt50k_frac
(opts)
facelit/metrics/metric_main.py:117
Functioneqt50k_int
(opts)
facelit/metrics/metric_main.py:111
Functionestimate_translation
Find camera translation that brings 3D joints S closest to 2D the corresponding joints_2d. Input: S: (B, 49, 3) 3D joint locations
facelit/geometry_utils.py:152
Methodextra_repr
(self)
facelit/training/networks_stylegan3.py:105
Methodextra_repr
(self)
facelit/training/networks_stylegan3.py:165
Methodextra_repr
(self)
facelit/training/networks_stylegan3.py:247
Methodextra_repr
(self)
facelit/training/networks_stylegan3.py:388
Methodextra_repr
(self)
facelit/training/networks_stylegan3.py:482
Methodextra_repr
(self)
facelit/training/superresolution.py:256
Methodextra_repr
(self)
facelit/training/networks_stylegan2.py:129
Methodextra_repr
(self)
facelit/training/networks_stylegan2.py:185
Methodextra_repr
(self)
facelit/training/networks_stylegan2.py:270
Methodextra_repr
(self)
facelit/training/networks_stylegan2.py:332
Methodextra_repr
(self)
facelit/training/networks_stylegan2.py:359
Methodextra_repr
(self)
facelit/training/networks_stylegan2.py:463
Methodextra_repr
(self)
facelit/training/networks_stylegan2.py:520
Methodextra_repr
(self)
facelit/training/networks_stylegan2.py:640
Methodextra_repr
(self)
facelit/training/networks_stylegan2.py:669
Methodextra_repr
(self)
facelit/training/networks_stylegan2.py:730
Methodextra_repr
(self)
facelit/training/networks_stylegan2.py:793
Methodextra_repr
(self)
facelit/training/dual_discriminator.py:81
Methodextra_repr
(self)
facelit/training/dual_discriminator.py:173
Methodextra_repr
(self)
facelit/training/dual_discriminator.py:246
Methodextra_repr
(self)
facelit/training/dual_discriminator.py:318
Functionfid50k
(opts)
facelit/metrics/metric_main.py:132
Functionfid50k_full
(opts)
facelit/metrics/metric_main.py:88
Functionfilter2d
r"""Filter a batch of 2D images using the given 2D FIR filter. By default, the result is padded so that its shape matches the input. User-spe
facelit/torch_utils/ops/upfirdn2d.py:279
Functionfiltered_lrelu
r"""Filtered leaky ReLU for a batch of 2D images. Performs the following sequence of operations for each channel: 1. Add channel-specific bi
facelit/torch_utils/ops/filtered_lrelu.py:58
Functionfiltered_lrelu
facelit/torch_utils/ops/filtered_lrelu.cpp:20
Functionfiltered_lrelu_act
facelit/torch_utils/ops/filtered_lrelu.cpp:217
Methodfind_class
(self, module, name)
facelit/legacy.py:68
Functionfma
(a, b, c)
facelit/torch_utils/ops/fma.py:17
Functionfolder_write_bytes
(fname: str, data: Union[bytes, str])
facelit/dataset_tool.py:310
Functionformat_time
Convert the seconds to human readable string with days, hours, minutes and seconds.
facelit/dnnlib/util.py:141
Functionformat_time_brief
Convert the seconds to human readable string with days, hours, minutes and seconds.
facelit/dnnlib/util.py:155
Methodforward
(ctx, x, fu, fd, b, si, sx, sy)
facelit/torch_utils/ops/filtered_lrelu.py:182
Methodforward
(ctx, input, grid)
facelit/torch_utils/ops/grid_sample_gradfix.py:42
Methodforward
(ctx, grad_output, input, grid)
facelit/torch_utils/ops/grid_sample_gradfix.py:59
Methodforward
(ctx, x, b)
facelit/torch_utils/ops/bias_act.py:146
Methodforward
(ctx, dy, x, b, y)
facelit/torch_utils/ops/bias_act.py:179
Methodforward
(ctx, x, f)
facelit/torch_utils/ops/upfirdn2d.py:235
Methodforward
(ctx, a, b, c)
facelit/torch_utils/ops/fma.py:24
Methodforward
(ctx, input, weight, bias)
facelit/torch_utils/ops/conv2d_gradfix.py:109
Methodforward
(ctx, grad_output, input, weight)
facelit/torch_utils/ops/conv2d_gradfix.py:157
Methodforward
(self, c)
facelit/metrics/perceptual_path_length.py:50
Methodforward
(self, x)
facelit/training/networks_stylegan3.py:91
Methodforward
(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False)
facelit/training/networks_stylegan3.py:137
Methodforward
(self, w)
facelit/training/networks_stylegan3.py:200
Methodforward
(self, x, w, noise_mode='random', force_fp32=False, update_emas=False)
facelit/training/networks_stylegan3.py:331
Methodforward
(self, ws, **layer_kwargs)
facelit/training/networks_stylegan3.py:466
Methodforward
(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False, **synthesis_kwargs)
facelit/training/networks_stylegan3.py:512
Methodforward
(self, rgb, x, ws, **block_kwargs)
facelit/training/superresolution.py:46
Methodforward
(self, rgb, x, ws, **block_kwargs)
facelit/training/superresolution.py:77
Methodforward
(self, rgb, x, ws, **block_kwargs)
facelit/training/superresolution.py:110
Methodforward
(self, rgb, x, ws, **block_kwargs)
facelit/training/superresolution.py:142
Methodforward
(self, x, img, ws, force_fp32=False, fused_modconv=None, update_emas=False, **layer_kwargs)
facelit/training/superresolution.py:210
Methodforward
(self, rgb, x, ws, **block_kwargs)
facelit/training/superresolution.py:280
Methodforward
(self, x)
facelit/training/networks_stylegan2.py:114
Methodforward
(self, x, gain=1)
facelit/training/networks_stylegan2.py:174
Methodforward
(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False)
facelit/training/networks_stylegan2.py:233
Methodforward
(self, x, w, noise_mode='random', fused_modconv=True, gain=1)
facelit/training/networks_stylegan2.py:311
Methodforward
(self, x, w, fused_modconv=True)
facelit/training/networks_stylegan2.py:353
Methodforward
(self, x, img, ws, force_fp32=False, fused_modconv=None, update_emas=False, **layer_kwargs)
facelit/training/networks_stylegan2.py:417
Methodforward
(self, ws, **block_kwargs)
facelit/training/networks_stylegan2.py:503
Methodforward
(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False, **synthesis_kwargs)
facelit/training/networks_stylegan2.py:549
Methodforward
(self, x, img, force_fp32=False)
facelit/training/networks_stylegan2.py:608
Methodforward
(self, x)
facelit/training/networks_stylegan2.py:652
Methodforward
(self, x, img, cmap, force_fp32=False)
facelit/training/networks_stylegan2.py:702
Methodforward
(self, img, c, update_emas=False, **block_kwargs)
facelit/training/networks_stylegan2.py:780
Methodforward
(self, z, c, truncation_psi=1, truncation_cutoff=None, neural_rendering_resolution=None, update_emas=False, ca
facelit/training/triplane.py:121
Methodforward
(self, sampled_features, ray_directions)
facelit/training/triplane.py:188
Methodforward
(self, sampled_features, ray_directions)
facelit/training/triplane.py:217
Methodforward
(self, sampled_features, ray_directions)
facelit/training/triplane.py:244
Methodforward
(self, images, debug_percentile=None)
facelit/training/augment.py:188
Methodforward
(self, img, c, update_emas=False, **block_kwargs)
facelit/training/dual_discriminator.py:66
Methodforward
(self, img, c, update_emas=False, **block_kwargs)
facelit/training/dual_discriminator.py:156
Methodforward
(self, img, c, update_emas=False, **block_kwargs)
facelit/training/dual_discriminator.py:228
Methodforward
(self, img, c, update_emas=False, **block_kwargs)
facelit/training/dual_discriminator.py:300
Methodforward
(self, planes, decoder, ray_origins, ray_directions, light, rendering_options, w_specular=1.)
facelit/training/volumetric_rendering/renderer.py:134
Methodforward
Create batches of rays and return origins and directions. cam2world_matrix: (N, 4, 4) intrinsics: (N, 3, 3) resoluti
facelit/training/volumetric_rendering/ray_sampler.py:24
Methodforward
(self, colors, densities, depths, rendering_options)
facelit/training/volumetric_rendering/ray_marcher.py:60
Functionget_dtype_and_ctype
Given a type name string (or an object having a __name__ attribute), return matching Numpy and ctypes types that have the same size in bytes.
facelit/dnnlib/util.py:203
Functionget_module_dir_by_obj_name
Get the directory path of the module containing the given object name.
facelit/dnnlib/util.py:308
Functionget_plugin
(module_name, sources, headers=None, source_dir=None, **build_kwargs)
facelit/torch_utils/custom_ops.py:61
Functionget_ray_limits_box
Author: Petr Kellnhofer Intersects rays with the [-1, 1] NDC volume. Returns min and max distance of entry. Returns -1 for no interse
facelit/training/volumetric_rendering/math_utils.py:46
Functionget_top_level_function_name
Return the fully-qualified name of a top-level function.
facelit/dnnlib/util.py:319
Functiongrid_sample
(input, grid)
facelit/torch_utils/ops/grid_sample_gradfix.py:28
Methodhas_labels
(self)
facelit/training/dataset.py:162
Methodhas_onehot_labels
(self)
facelit/training/dataset.py:166
Methodimage_shape
(self)
facelit/training/dataset.py:132
Functionimport_hook
r"""Register an import hook that is called whenever a persistent object is being unpickled. A typical use case is to patch the pickled source
facelit/torch_utils/persistence.py:149
Methodinit_args
(self)
facelit/torch_utils/persistence.py:113
Methodinit_kwargs
(self)
facelit/torch_utils/persistence.py:117
Functioninit_multiprocessing
r"""Initializes `torch_utils.training_stats` for collecting statistics across multiple processes. This function must be called after `tor
facelit/torch_utils/training_stats.py:36
Functionis50k
(opts)
facelit/metrics/metric_main.py:150
Functionis_pickleable
(obj: Any)
facelit/dnnlib/util.py:226
Functionkid50k
(opts)
facelit/metrics/metric_main.py:138
Functionkid50k_full
(opts)
facelit/metrics/metric_main.py:94
Methodlabel_dim
(self)
facelit/training/dataset.py:157
Methodlabel_shape
(self)
facelit/training/dataset.py:147
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