pytorch3d.implicitron.models.renderer.rgb_net

rgb_net

class pytorch3d.implicitron.models.renderer.rgb_net.RayNormalColoringNetwork(feature_vector_size: int = 3, mode: str = 'idr', d_in: int = 9, d_out: int = 3, dims: Tuple[int, ...] = (512, 512, 512, 512), weight_norm: bool = True, n_harmonic_functions_dir: int = 0, pooled_feature_dim: int = 0)[source]

Bases: Module

Members:
d_in and feature_vector_size: Sum of these is the input
dimension. These must add up to the sum of

  • 3 [for the points]

  • 3 unless mode=no_normal [for the normals]

  • 3 unless mode=no_view_dir [for view directions]

  • the feature size, [number of channels in feature_vectors]

d_out: dimension of output. mode: One of “idr”, “no_view_dir” or “no_normal” to allow omitting

part of the network input.

dims: list of hidden layer sizes. weight_norm: whether to apply weight normalization to each layer. n_harmonic_functions_dir:

If >0, use a harmonic embedding with this number of harmonic functions for the view direction. Otherwise view directions are fed without embedding, unless mode is no_view_dir.

pooled_feature_dim: If a pooling function is in use (provided as

pooling_fn to forward()) this must be its number of features. Otherwise this must be set to 0. (If used from GenericModel, this will be set automatically.)

forward(feature_vectors: Tensor, points, normals, ray_bundle: ImplicitronRayBundle, masks=None, pooling_fn=None)[source]