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Functions563 in github.com/SizheAn/PanoHead

⨍ Functions 563◇ Types & classes 69↳ Endpoints 14
↓ 2 callersMethodget_all_torch
(self)
metrics/metric_utils.py:131
↓ 2 callersFunctionget_feature_detector
(url, device=torch.device('cpu'), num_gpus=1, rank=0, verbose=False)
metrics/metric_utils.py:46
↓ 2 callersFunctionget_mask
(model, batch, cid)
calc_mbs.py:54
↓ 2 callersMethodget_mean_cov
(self)
metrics/metric_utils.py:134
↓ 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:238
↓ 2 callersFunctionget_pil_font
(font=None, size=32)
gui_utils/text_utils.py:31
↓ 2 callersFunctionget_poseangle
(eg3dparams)
training/utils.py:66
↓ 2 callersFunctiongrayed_out
(cond=True)
gui_utils/imgui_utils.py:44
↓ 2 callersMethodis_compatible
(self, *, image=None, width=None, height=None, channels=None, dtype=None)
gui_utils/gl_utils.py:203
↓ 2 callersFunctionis_image_ext
(fname: Union[str, Path])
dataset_tool_seg.py:59
↓ 2 callersFunctionis_image_ext
(fname: Union[str, Path])
dataset_tool.py:63
↓ 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:136
↓ 2 callersFunctionis_valid_metric
(metric)
metrics/metric_main.py:36
↓ 2 callersFunctionmake_transform
( transform: Optional[str], output_width: Optional[int], output_height: Optional[int], resampl
dataset_tool_seg.py:116
↓ 2 callersFunctionmodulated_conv2d
( x, # Input tensor of shape [batch_size, in_channels, in_height, in_width].
training/networks_stylegan2.py:34
↓ 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:143
↓ 2 callersFunctionnorm_ip
(img, low, high)
calc_mbs.py:71
↓ 2 callersFunctionnorm_ip
(img, low, high)
gen_interpolation.py:65
↓ 2 callersFunctionnorm_range
(t, value_range=(-1, 1))
calc_mbs.py:77
↓ 2 callersFunctionnormalize_2nd_moment
(x, dim=1, eps=1e-8)
training/networks_stylegan2.py:28
↓ 2 callersFunctionopen_dest
(dest: str)
dataset_tool_seg.py:168
↓ 2 callersMethodrun_G
(self, z, c, swapping_prob, neural_rendering_resolution, update_emas=False)
training/loss.py:55
↓ 2 callersMethodsample_stratified
Return depths of approximately uniformly spaced samples along rays.
training/volumetric_rendering/renderer.py:239
↓ 2 callersMethodset_window_size
(self, width, height)
gui_utils/glfw_window.py:101
↓ 2 callersFunctionslerp
(a, b, t)
metrics/perceptual_path_length.py:24
↓ 2 callersFunctionsuppress_tracer_warnings
()
torch_utils/misc.py:73
↓ 2 callersFunctiontriplane_crop_mask
(xyz_unformatted, thresh, boxwarp, allow_bottom=True)
training/volumetric_rendering/renderer.py:75
↓ 1 callersMethod__getstate__
(self)
training/dataset.py:77
↓ 1 callersMethod__init__
(self, n_features, options)
training/triplane.py:166
↓ 1 callersMethod__init__
(self, *, title='ImguiWindow', font=None, font_sizes=range(14,24), **glfw_kwargs)
gui_utils/imgui_window.py:22
↓ 1 callersMethod__reduce__
(self)
torch_utils/persistence.py:120
↓ 1 callersFunction_bias_act_cuda
Fast CUDA implementation of `bias_act()` using custom ops.
torch_utils/ops/bias_act.py:128
↓ 1 callersFunction_bias_act_ref
Slow reference implementation of `bias_act()` using standard TensorFlow ops.
torch_utils/ops/bias_act.py:93
↓ 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:233
↓ 1 callersMethod_filter_samples
(self)
training/dataset.py:65
↓ 1 callersFunction_filtered_lrelu_ref
Slow and memory-inefficient reference implementation of `filtered_lrelu()` using existing `upfirdn2n()` and `bias_act()` ops.
torch_utils/ops/filtered_lrelu.py:123
↓ 1 callersFunction_find_compiler_bindir
()
torch_utils/custom_ops.py:31
↓ 1 callersFunction_get_array_impl
(string, *, font=None, size=32, outline=0, outline_pad=3, outline_coef=3, outline_exp=2, line_pad: int=None)
gui_utils/text_utils.py:76
↓ 1 callersFunction_get_array_impl_dropshadow
(string, *, font=None, size=32, radius: int, offset_x: int, offset_y: int, line_pad: int=None, **kwargs)
gui_utils/text_utils.py:98
↓ 1 callersFunction_get_mangled_gpu_name
()
torch_utils/custom_ops.py:46
↓ 1 callersFunction_init
()
torch_utils/ops/filtered_lrelu.py:25
↓ 1 callersFunction_init
()
torch_utils/ops/bias_act.py:40
↓ 1 callersFunction_init
()
torch_utils/ops/upfirdn2d.py:25
↓ 1 callersMethod_load_raw_image
(self, raw_idx)
training/dataset.py:217
↓ 1 callersMethod_load_raw_labels
(self)
training/dataset.py:74
↓ 1 callersFunction_module_to_src
r"""Query the source code of a given Python module.
torch_utils/persistence.py:208
↓ 1 callersFunction_setup_circle
(hole)
gui_utils/gl_utils.py:370
↓ 1 callersFunction_setup_rect
(rx, ry)
gui_utils/gl_utils.py:354
↓ 1 callersFunction_should_use_custom_op
()
torch_utils/ops/grid_sample_gradfix.py:35
↓ 1 callersFunction_src_to_module
r"""Get or create a Python module for the given source code.
torch_utils/persistence.py:218
↓ 1 callersFunction_sync
r"""Synchronize the global cumulative counters across devices and processes. Called internally by `Collector.update()`.
torch_utils/training_stats.py:236
↓ 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, real_c, gen_z, gen_c, gain, cur_nimg)
training/loss.py:18
↓ 1 callersFunctionapply_affine_transformation
(x, mat, up=4, **filter_kwargs)
metrics/equivariance.py:137
↓ 1 callersFunctionapply_fractional_pseudo_rotation
(x, angle, a=3, **filter_kwargs)
metrics/equivariance.py:181
↓ 1 callersFunctionapply_fractional_rotation
(x, angle, a=3, **filter_kwargs)
metrics/equivariance.py:171
↓ 1 callersFunctionapply_fractional_translation
(x, tx, ty, a=3)
metrics/equivariance.py:65
↓ 1 callersFunctionapply_integer_translation
(x, tx, ty)
metrics/equivariance.py:46
↓ 1 callersMethodbegin_frame
(self)
gui_utils/glfw_window.py:163
↓ 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:101
↓ 1 callersFunctioncall_func_by_name
Finds the python object with the given name and calls it as a function.
dnnlib/util.py:295
↓ 1 callersMethodclose
(self)
gui_utils/glfw_window.py:47
↓ 1 callersFunctionconvert_dataset
Convert an image dataset into a dataset archive usable with StyleGAN2 ADA PyTorch. The input dataset format is guessed from the --source argument
dataset_tool_seg.py:211
↓ 1 callersFunctionconvert_dataset
Convert an image dataset into a dataset archive usable with StyleGAN2 ADA PyTorch. The input dataset format is guessed from the --source argument
dataset_tool.py:332
↓ 1 callersFunctionconvert_network_pickle
Convert legacy network pickle into the native PyTorch format. The tool is able to load the main network configurations exported using the TensorF
legacy.py:299
↓ 1 callersFunctionconvert_tf_discriminator
(tf_D)
legacy.py:210
↓ 1 callersFunctioncreate_samples
(N=256, voxel_origin=[0, 0, 0], cube_length=2.0)
gen_videos.py:43
↓ 1 callersFunctioncreate_samples
(N=256, voxel_origin=[0, 0, 0], cube_length=2.0)
gen_samples_forID.py:74
↓ 1 callersFunctioncreate_samples
(N=256, voxel_origin=[0, 0, 0], cube_length=2.0)
gen_videos_proj_withseg.py:42
↓ 1 callersFunctioncreate_samples
(N=256, voxel_origin=[0, 0, 0], cube_length=2.0)
gen_samples.py:74
↓ 1 callersFunctioncreate_samples
(N=256, voxel_origin=[0, 0, 0], cube_length=2.0)
gen_videos_proj.py:42
↓ 1 callersFunctioncreate_samples
(N=256, voxel_origin=[0, 0, 0], cube_length=2.0)
gen_videos_interp.py:43
↓ 1 callersFunctioncreate_samples
(N=256, voxel_origin=[0, 0, 0], cube_length=2.0)
projector_withseg.py:22
↓ 1 callersFunctioncrop_final
( img, size=512, quad=None, top_expand=0.1, left_expand=0.05, bottom_expand=0.0,
3DDFA_V2_cropping/recrop_images.py:104
↓ 1 callersFunctioncrop_image
(img, mat, crop_w, crop_h, upsample=1, borderMode=cv2.BORDER_CONSTANT)
3DDFA_V2_cropping/recrop_images.py:93
↓ 1 callersMethoddelete
(self)
gui_utils/gl_utils.py:163
↓ 1 callersMethoddelete
(self)
gui_utils/gl_utils.py:274
↓ 1 callersFunctiondraw_rect
(*, pos=0, pos2=None, size=None, align=0, rint=False, color=1, alpha=1, rounding=0)
gui_utils/gl_utils.py:337
↓ 1 callersFunctioneg3dcamparams
(R_in)
3DDFA_V2_cropping/recrop_images.py:26
↓ 1 callersFunctionfind_center_bbox
(roi_box_lst, w, h)
3DDFA_V2_cropping/recrop_images.py:160
↓ 1 callersFunctiongen_interp_video
(G, mp4: str, ws, w_frames=60*4, kind='cubic', grid_dims=(1,1), num_keyframes=None, wraps=2, psi=1, truncation
gen_videos_proj_withseg.py:68
↓ 1 callersFunctiongen_interp_video
(G, mp4: str, ws, w_frames=60*4, kind='cubic', grid_dims=(1,1), num_keyframes=None, wraps=2, psi=1, truncation
gen_videos_proj.py:68
↓ 1 callersFunctiongenerate_images
Render a latent vector interpolation video. Examples: \b # Render a 4x2 grid of interpolations for seeds 0 through 31. python gen_vi
gen_videos.py:284
↓ 1 callersFunctiongenerate_images
Generate images using pretrained network pickle. Examples: \b # Generate an image using pre-trained FFHQ model. python gen_samples.p
gen_samples_forID.py:114
↓ 1 callersFunctiongenerate_images
Render a latent vector interpolation video.
gen_videos_proj_withseg.py:252
↓ 1 callersFunctiongenerate_images
Generate images using pretrained network pickle. Examples: \b # Generate an image using pre-trained model. python gen_samples.py --o
gen_samples.py:114
↓ 1 callersFunctiongenerate_images
Render a latent vector interpolation video.
gen_videos_proj.py:231
↓ 1 callersFunctiongenerate_images
Render a latent vector interpolation video. Examples: \b # Render a 4x2 grid of interpolations for seeds 0 through 31. python gen_vi
gen_videos_interp.py:286
↓ 1 callersFunctiongenerate_images
Generate images using pretrained network pickle. Examples: \b # Generate an image using pre-trained FFHQ model. python gen_samples.p
calc_mbs.py:92
↓ 1 callersFunctiongenerate_images
Generate interpolation images using pretrained network pickle. Examples: \b python gen_interpolation.py --network models/easy-khair-180-
gen_interpolation.py:87
↓ 1 callersFunctiongenerate_planes
Defines planes by the three vectors that form the "axes" of the plane. Should work with arbitrary number of planes and planes of arbitrar
training/volumetric_rendering/renderer.py:15
↓ 1 callersFunctionget_array
(string, *, dropshadow_radius: int=None, **kwargs)
gui_utils/text_utils.py:38
↓ 1 callersFunctionget_crop_bound
(lm, method="ffhq")
3DDFA_V2_cropping/recrop_images.py:51
↓ 1 callersFunctionget_default_font
()
gui_utils/text_utils.py:24
↓ 1 callersMethodget_details
(self, idx)
training/dataset.py:107
↓ 1 callersFunctionget_feature_detector_name
(url)
metrics/metric_utils.py:43
↓ 1 callersMethodget_label_std
(self)
training/dataset.py:114
↓ 1 callersFunctionget_mask
(model, batch, cid)
misc/segmentation_example.py:9
↓ 1 callersFunctionget_obj_by_name
Finds the python object with the given name.
dnnlib/util.py:289
↓ 1 callersFunctioninit_dataset_kwargs
(img_data, seg_data, min_yaw=None, max_yaw=None, back_repeat=None)
train.py:100
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