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Types & classes40 in github.com/QiuhongAnnaWei/LEGO-Net

⨍ Functions 328◇ Types & classes 40
↓ 6 callersClassMLP_stacked
model/layers.py:304
↓ 6 callersClassSphericalHarmonicsCoeffs
Spherical Harmonics Transform layer
ConDor_torch/spherical_harmonics/spherical_cnn.py:248
↓ 5 callersClassEmbedding
model/layers.py:212
↓ 5 callersClassMLP
ConDor_torch/models/layers.py:42
↓ 5 callersClassMLP_layer
ConDor_torch/models/layers.py:62
↓ 5 callersClassPointNetSetAbstraction
model/layers.py:162
↓ 5 callersClassTDFDataset
3D-Front Dataset
data/TDFront.py:44
↓ 4 callersClassTransformerWrapper
model/transformer.py:103
↓ 3 callersClassFixedPositionalEncoding
model/transformer.py:57
↓ 3 callersClassGroupPoints
ConDor_torch/utils/group_points.py:107
↓ 3 callersClassTransformer
model/transformer.py:87
↓ 3 callersClasstorch_spherical_harmonics
ConDor_torch/spherical_harmonics/spherical_cnn.py:104
↓ 2 callersClassAttention_block
model/layers.py:247
↓ 2 callersClassMLP_layer
model/layers.py:278
↓ 2 callersClassShGaussianKernelConv
ConDor_torch/spherical_harmonics/kernels.py:113
↓ 2 callersClassSphericalHarmonicsEval
Inverse Spherical Harmonics Transform layer
ConDor_torch/spherical_harmonics/spherical_cnn.py:210
↓ 2 callersClassSphericalHarmonicsGaussianKernels
Computes steerable kernel for point clouds. Eqn. 13 and Eqn. 4 of paper https://openaccess.thecvf.com/content/CVPR2021/papers/Poule
ConDor_torch/spherical_harmonics/kernels.py:316
↓ 2 callersClassTFN
TFN layer for prediction in Pytorch
ConDor_torch/models/TFN.py:8
↓ 2 callersClasstransform
model/models.py:201
↓ 1 callersClassConDor
ConDor_torch/models/ConDor.py:10
↓ 1 callersClassEmbedder
model/transformer.py:5
↓ 1 callersClassH5Loader
ConDor_torch/datasets/h5_dataset.py:31
↓ 1 callersClassPointNetPlusPlus_attention
model/models.py:283
↓ 1 callersClassPointNetPlusPlus_dense
model/models.py:389
↓ 1 callersClassPointNetPlusPlus_dense_attention
model/models.py:440
↓ 1 callersClassPointNetSeg
Pointnet segmentation network
model/models.py:89
↓ 1 callersClassPointNet_Line
The 'pointnet' encoder in train script. Has 3 sections: point processing, line processing, floor plan feature processing
model/floorplan_encoder.py:38
↓ 1 callersClassPointNet_Point
The 'pointnet_simple' encoder in train script.
model/floorplan_encoder.py:5
↓ 1 callersClassPointTransformer
Point Transformer network
model/models.py:493
↓ 1 callersClassResNet18
using the ResNet18 architecture, refeferencing ATISS's choice of layout encoder.
model/floorplan_encoder.py:109
↓ 1 callersClasstorch_clebsch_gordan_decomposition
ConDor_torch/spherical_harmonics/clebsch_gordan_decomposition.py:197
↓ 1 callersClasszernike_monoms
ConDor_torch/spherical_harmonics/spherical_cnn.py:69
ClassConDor_trainer
Segmentation trainer to mimic NeSF
ConDor_torch/trainers/ConDor_trainer.py:19
ClassDiscriminator
model/models.py:11
ClassDiscriminator_attention_positional_position
positional_position: xy coords alone, no angle
model/models.py:247
ClassPointNetClass
Pointnet classification network
model/models.py:39
ClassPointNetClass_transform
model/models.py:143
ClassPointNetPlusPlus
model/models.py:327
ClassTransformerWrapper_Simple
model/transformer.py:310
ClassTransformerWrapper_SimpleSize
Also pass siz in [pos, ang, siz, cla] into PE (compared to only passing pos+ang).
model/transformer.py:342