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Functions4,689 in github.com/alibaba/Taobao3D

⨍ Functions 4,689◇ Types & classes 1,343↳ Endpoints 1

(self, writer, name, step=None, write=True)

Dens3R/AutoReconForDens3R/nerfstudio/utils/writer.py:255

(self, log_dir: Path, experiment_name: str)

Dens3R/AutoReconForDens3R/nerfstudio/utils/writer.py:287

(self, log_dir: Path)

Dens3R/AutoReconForDens3R/nerfstudio/utils/writer.py:312

(self, config: cfg.LocalWriterConfig, banner_messages: Optional[List[str]] = None)

Dens3R/AutoReconForDens3R/nerfstudio/utils/writer.py:370

(self, images: List)

Dens3R/AutoReconForDens3R/nerfstudio/utils/images.py:39

(self, im, sigma_spatial=32, sigma_luma=8, sigma_chroma=8)

Dens3R/AutoReconForDens3R/nerfstudio/utils/bilateral_solver.py:36

(self, grid, params)

Dens3R/AutoReconForDens3R/nerfstudio/utils/bilateral_solver.py:118

(self, suffix="it/s")

Dens3R/AutoReconForDens3R/nerfstudio/utils/rich_utils.py:37

(self, config: cfg.LoggingConfig)

Dens3R/AutoReconForDens3R/nerfstudio/utils/profiler.py:91

( self, fg_pts: Optional[torch.Tensor] = None, bg_pts: Optional[torch.Tensor] = None,

Dens3R/AutoReconForDens3R/nerfstudio/utils/pointclouds.py:33

( self, num_rays_per_batch: int, keep_full_image: bool = False, mask_sample_st

Dens3R/AutoReconForDens3R/nerfstudio/data/pixel_samplers.py:364

( self, dataset: Dataset, num_images_to_sample_from: int = -1, num_times_to_re

Dens3R/AutoReconForDens3R/nerfstudio/data/utils/dataloaders.py:51

( self, input_dataset: InputDataset, image_indices: Optional[Tuple[int]] = None,

Dens3R/AutoReconForDens3R/nerfstudio/data/utils/dataloaders.py:206

( self, input_dataset: InputDataset, image_indices: Optional[Tuple[int]] = None,

Dens3R/AutoReconForDens3R/nerfstudio/data/utils/dataloaders.py:240

( self, config: VanillaDataManagerConfig, device: Union[torch.device, str] = "cpu",

Dens3R/AutoReconForDens3R/nerfstudio/data/datamanagers/base_datamanager.py:321

(self, config: DataParserConfig)

Dens3R/AutoReconForDens3R/nerfstudio/data/dataparsers/base_dataparser.py:103

(self, config: BlenderDataParserConfig)

Dens3R/AutoReconForDens3R/nerfstudio/data/dataparsers/blender_dataparser.py:59

(self, config: PhototourismDataParserConfig)

Dens3R/AutoReconForDens3R/nerfstudio/data/dataparsers/phototourism_dataparser.py:73

(self, config: DNeRFDataParserConfig)

Dens3R/AutoReconForDens3R/nerfstudio/data/dataparsers/dnerf_dataparser.py:57

(self, config: HeritageDataParserConfig)

Dens3R/AutoReconForDens3R/nerfstudio/data/dataparsers/heritage_dataparser.py:103

(self, dataparser_outputs: DataparserOutputs, scale_factor: float = 1.0)

Dens3R/AutoReconForDens3R/nerfstudio/data/datasets/base_dataset.py:156

(self, dataparser_outputs: DataparserOutputs, scale_factor: float = 1.0)

Dens3R/AutoReconForDens3R/nerfstudio/data/datasets/semantic_dataset.py:35

(self, dataparser_outputs: DataparserOutputs, scale_factor: float = 1.0)

Dens3R/AutoReconForDens3R/nerfstudio/data/datasets/autorecon_dataset.py:22

(self, in_dim: Optional[int] = None, activation: Optional[nn.Module] = nn.Softplus())

Dens3R/AutoReconForDens3R/nerfstudio/field_components/field_heads.py:109

(self, in_dim: Optional[int] = None, activation: Optional[nn.Module] = nn.Sigmoid())

Dens3R/AutoReconForDens3R/nerfstudio/field_components/field_heads.py:121

( self, in_dim: Optional[int] = None, levels: int = 3, channels: int = 3, activation: Optional[nn.Modu

Dens3R/AutoReconForDens3R/nerfstudio/field_components/field_heads.py:135

(self, in_dim: Optional[int] = None, activation: Optional[nn.Module] = nn.Softplus())

Dens3R/AutoReconForDens3R/nerfstudio/field_components/field_heads.py:151

(self, in_dim: Optional[int] = None, activation: Optional[nn.Module] = nn.Sigmoid())

Dens3R/AutoReconForDens3R/nerfstudio/field_components/field_heads.py:163

(self, in_dim: Optional[int] = None, activation: Optional[nn.Module] = nn.Softplus())

Dens3R/AutoReconForDens3R/nerfstudio/field_components/field_heads.py:175

(self, num_classes: int, in_dim: Optional[int] = None)

Dens3R/AutoReconForDens3R/nerfstudio/field_components/field_heads.py:190

(self, in_dim: Optional[int] = None, activation: Optional[nn.Module] = nn.Tanh())

Dens3R/AutoReconForDens3R/nerfstudio/field_components/field_heads.py:202

(self, in_dim: int, scaling: float = 1.0, offset: float = 0.0)

Dens3R/AutoReconForDens3R/nerfstudio/field_components/encodings.py:84

( self, in_dim: int, num_frequencies: int, min_freq_exp: float, max_freq_exp: float, include_input: bo

Dens3R/AutoReconForDens3R/nerfstudio/field_components/encodings.py:111

(self, in_dim: int, num_frequencies: int, scale: float, include_input: bool = False)

Dens3R/AutoReconForDens3R/nerfstudio/field_components/encodings.py:172

( self, num_levels: int = 16, min_res: int = 16, max_res: int = 1024,

Dens3R/AutoReconForDens3R/nerfstudio/field_components/encodings.py:234

(self, resolution: int = 256, num_components: int = 24, init_scale: float = 0.1)

Dens3R/AutoReconForDens3R/nerfstudio/field_components/encodings.py:366

( self, resolution: int = 128, num_components: int = 24, init_scale: float = 0

Dens3R/AutoReconForDens3R/nerfstudio/field_components/encodings.py:419

(self, levels: int = 4)

Dens3R/AutoReconForDens3R/nerfstudio/field_components/encodings.py:552

( self, num_levels: int = 16, min_res: int = 16, max_res: int = 1024,

Dens3R/AutoReconForDens3R/nerfstudio/field_components/encodings.py:581

( self, encoding: tcnn.Encoding, n_levels: int = 16, # number of resolution levels

Dens3R/AutoReconForDens3R/nerfstudio/field_components/progressive_encoding.py:14

( self, order: Optional[Union[float, int]] = None, aabb: Optional[torch.Tensor] = None

Dens3R/AutoReconForDens3R/nerfstudio/field_components/spatial_distortions.py:116

( self, in_dim: int, num_layers: int, layer_width: int, out_dim: Optio

Dens3R/AutoReconForDens3R/nerfstudio/field_components/mlp.py:39

(self, in_dim: Optional[int] = None, out_dim: Optional[int] = None)

Dens3R/AutoReconForDens3R/nerfstudio/field_components/base_field_component.py:33

( self, position_encoding: Encoding = NeRFEncoding( in_dim=3, num_frequencies=10,

Dens3R/AutoReconForDens3R/nerfstudio/field_components/temporal_distortions.py:64

(self, in_dim: int, out_dim: int)

Dens3R/AutoReconForDens3R/nerfstudio/field_components/embedding.py:35

(self, optimizer, lr_init, lr_final, max_steps, lr_delay_steps=0, lr_delay_mult=1.0)

Dens3R/AutoReconForDens3R/nerfstudio/engine/schedulers.py:58

( self, optimizer: Optimizer, lr_init, # pylint: disable=unused-argument lr_f

Dens3R/AutoReconForDens3R/nerfstudio/engine/schedulers.py:79

( self, optimizer: Optimizer, lr_init, lr_final, max_steps, de

Dens3R/AutoReconForDens3R/nerfstudio/engine/schedulers.py:102

(self, optimizer, warm_up_end)

Dens3R/AutoReconForDens3R/nerfstudio/engine/schedulers.py:206

( self, where_to_run: List[TrainingCallbackLocation], func: Callable, update_e

Dens3R/AutoReconForDens3R/nerfstudio/engine/callbacks.py:66

(self, config: cfg.Config, local_rank: int = 0, world_size: int = 1)

Dens3R/AutoReconForDens3R/nerfstudio/engine/trainer.py:74

(self, config: Dict[str, Any], param_groups: Dict[str, List[Parameter]])

Dens3R/AutoReconForDens3R/nerfstudio/engine/optimizers.py:112

(self, entries: Tuple = tuple())

Dens3R/AutoReconForDens3R/nerfstudio/viewer/server/path.py:33

Dens3R/AutoReconForDens3R/nerfstudio/viewer/server/video_stream.py:25

(self, zmq_port, ip_address="127.0.0.1")

Dens3R/AutoReconForDens3R/nerfstudio/viewer/server/visualizer.py:41

( self, zmq_port: Optional[int] = None, window: Optional[ViewerWindow] = None, ip_address: str = "127.

Dens3R/AutoReconForDens3R/nerfstudio/viewer/server/visualizer.py:119

(self, *args, **kwargs)

Dens3R/AutoReconForDens3R/nerfstudio/viewer/server/server.py:38

(self, zmq_port: int, websocket_port: int, ip_address: str)

Dens3R/AutoReconForDens3R/nerfstudio/viewer/server/server.py:103

(self, state: "ViewerState", graph: Model, camera_ray_bundle: RayBundle)

Dens3R/AutoReconForDens3R/nerfstudio/viewer/server/viewer_utils.py:131

Dens3R/AutoReconForDens3R/nerfstudio/viewer/server/viewer_utils.py:172

(self, config: cfg.ViewerConfig, log_filename: Path)

Dens3R/AutoReconForDens3R/nerfstudio/viewer/server/viewer_utils.py:229

(self, *args, **kwargs)

Dens3R/AutoReconForDens3R/nerfstudio/viewer/server/state/node.py:28

(self, *args, **kwargs)

Dens3R/AutoReconForDens3R/nerfstudio/viewer/server/state/state_node.py:25

( self, config: DynamicBatchPipelineConfig, device: str, test_mode: Literal["t

Dens3R/AutoReconForDens3R/nerfstudio/pipelines/dynamic_batch.py:49

( self, config: VanillaPipelineConfig, device: str, test_mode: Literal["test",

Dens3R/AutoReconForDens3R/nerfstudio/pipelines/base_pipeline.py:214

Initializes the Cameras object. Note on Input Tensor Dimensions: All of these tensors have items of dimensions TensorType[3, 4] (in t

Dens3R/AutoReconForDens3R/nerfstudio/cameras/cameras.py:89

( self, config: CameraOptimizerConfig, num_cameras: int, device: Union[torch.d

Dens3R/AutoReconForDens3R/nerfstudio/cameras/camera_optimizers.py:69

(self, *args, **kwargs)

Dens3R/AutoReconForDens3R/nerfstudio/models/base_surface_model.py:220

(self, config: SemanticNerfWModelConfig, metadata: Dict, **kwargs)

Dens3R/AutoReconForDens3R/nerfstudio/models/semantic_nerfw.py:80

( self, config: TensoRFModelConfig, **kwargs, )

Dens3R/AutoReconForDens3R/nerfstudio/models/tensorf.py:84

( self, config: ModelConfig, scene_box: SceneBox, num_train_data: int,

Dens3R/AutoReconForDens3R/nerfstudio/models/base_model.py:70

( self, config: VanillaModelConfig, **kwargs, )

Dens3R/AutoReconForDens3R/nerfstudio/models/vanilla_nerf.py:70

(self, config: InstantNGPModelConfig, **kwargs)

Dens3R/AutoReconForDens3R/nerfstudio/models/instant_ngp.py:95

( self, config: ModelConfig, **kwargs, )

Dens3R/AutoReconForDens3R/nerfstudio/models/mipnerf.py:50

( self, position_encoding: Encoding = Identity(in_dim=3), direction_encoding: Encoding

Dens3R/AutoReconForDens3R/nerfstudio/fields/vanilla_nerf_field.py:54

( self, aabb, num_layers: int = 2, hidden_dim: int = 64, geo_feat_dim:

Dens3R/AutoReconForDens3R/nerfstudio/fields/instant_ngp_field.py:66

(self, init_val, beta_min=0.0001)

Dens3R/AutoReconForDens3R/nerfstudio/fields/sdf_field.py:57

(self, init_val, beta_min=0.0001)

Dens3R/AutoReconForDens3R/nerfstudio/fields/sdf_field.py:82

(self, init_val)

Dens3R/AutoReconForDens3R/nerfstudio/fields/sdf_field.py:116

( self, aabb, # the aabb bounding box of the dataset feature_encoding: Encodin

Dens3R/AutoReconForDens3R/nerfstudio/fields/tensorf_field.py:36

( self, aabb, num_layers: int = 2, hidden_dim: int = 64, spatial_disto

Dens3R/AutoReconForDens3R/nerfstudio/fields/density_fields.py:54

Dens3R/AutoReconForDens3R/nerfstudio/fields/base_field.py:43

( self, aabb, num_images: int, position_encoding: Encoding = HashEncoding(),

Dens3R/AutoReconForDens3R/nerfstudio/fields/nerfacto_field.py:351

(self, config: FeatureFieldConfig, ptcd_data: BasicPointClouds, aabb: TensorType[2, 3])

Dens3R/AutoReconForDens3R/nerfstudio/fields/feature_field.py:213

( self, num_images: int, position_encoding: Encoding = Identity(in_dim=3), dir

Dens3R/AutoReconForDens3R/nerfstudio/fields/nerfw_field.py:54

( self, num_semantic_classes: int, position_encoding: Encoding = Identity(in_dim=3),

Dens3R/AutoReconForDens3R/nerfstudio/fields/semantic_nerf_field.py:50

(self, patch_size: int = 31, pixel_offset: float = 0.5, valid_angle_thres: float = 0.3)

Dens3R/AutoReconForDens3R/nerfstudio/model_components/patch_warping.py:143

(self, scene_box: SceneBox, near_plane: float = 0.0, **kwargs)

Dens3R/AutoReconForDens3R/nerfstudio/model_components/scene_colliders.py:54

(self, near_plane: float, far_plane: float, **kwargs)

Dens3R/AutoReconForDens3R/nerfstudio/model_components/scene_colliders.py:119

(self, radius: float = 1., near_plane: float = 0.1, far_plane: float = 1000.0, **kwargs)

Dens3R/AutoReconForDens3R/nerfstudio/model_components/scene_colliders.py:136

(self, scene_box: SceneBox, near_plane: float = 0.1, far_plane: float = 1000.0, **kwargs)

Dens3R/AutoReconForDens3R/nerfstudio/model_components/scene_colliders.py:171

(self, reduction="batch-based")

Dens3R/AutoReconForDens3R/nerfstudio/model_components/losses.py:292

(self, scales=4, reduction="batch-based")

Dens3R/AutoReconForDens3R/nerfstudio/model_components/losses.py:305

(self, alpha=0.5, scales=4, reduction="batch-based")

Dens3R/AutoReconForDens3R/nerfstudio/model_components/losses.py:332

(self, patch_size)

Dens3R/AutoReconForDens3R/nerfstudio/model_components/losses.py:365

(self, patch_size: int = 11, topk: int = 4, min_patch_variance: float = 0.01)

Dens3R/AutoReconForDens3R/nerfstudio/model_components/losses.py:435

(self, truncation: float)

Dens3R/AutoReconForDens3R/nerfstudio/model_components/losses.py:572

( self, background_color: Union[Literal["random", "last_sample"], TensorType[3]] = "random"

Dens3R/AutoReconForDens3R/nerfstudio/model_components/renderers.py:52

(self, method: Literal["median", "expected"] = "median")

Dens3R/AutoReconForDens3R/nerfstudio/model_components/renderers.py:220

( self, spacing_fn: Callable, spacing_fn_inv: Callable, num_samples: Optional[

Dens3R/AutoReconForDens3R/nerfstudio/model_components/ray_samplers.py:75

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