src.model.deeplearn.layer

The layer package contains the logic to handle the layers of a neural network.

Modules

`contextual_point_layer`
`dense_with_ground_row`
`downsampling_spconv3d_layer`
`drop_ground_row`
`expansion_layer`
`features_downsampling_layer`
`features_structuring_layer`
`features_upsampling_layer`
`geometric_affine_layer`
`grouped_point_transformer_layer`
`grouping_point_net_layer`
`hourglass_layer`
`interdimensional_point_transformer_layer`
`kpconv_layer`
`kpconvx_layer`
`layer`
`light_kpconv_layer`
`masked_batch_normalization`
`point_mlp_layer`
`point_transformer_layer`
`rbf_feat_extract_layer`
`rbf_feat_processing_layer`
`spconv3d_decoding_layer`
`spconv3d_encoding_layer`
`strided_kpconv_layer`
`strided_light_kpconv_layer`
`submanifold_spconv3d_layer`
`torf_cast_float_layer`
`torf_element_divide_layer`
`torf_gva_layer`
`torf_masked_fill_layer`
`torf_output_mix_layer`
`torf_point_wise_mask_layer`
`torf_reduce_max_layer`
`torf_reduce_sum_layer`
`torf_slice_center_layer`
`upsampling_spconv3d_layer`