class_docstr: PointNet2 with multi-scale grouping
  Semantic segmentation network that uses feature propogation layers
  
  Parameters
  ----------
  num_classes: int
      Number of semantics classes to predict over -- size of softmax classifier that run for each point
  input_channels: int = 6
      Number of input channels in the feature descriptor for each point.  If the point cloud is Nx9, this
      value should be 6 as in an Nx9 point cloud, 3 of the channels are xyz, and 6 are feature descriptors
  use_xyz: bool = True
      Whether or not to use the xyz position of a point as a feature
class_docstr_tok: ["PointNet2","with","multi-scale","grouping","Semantic","segmentation","network","that","uses","feature","propogation","layers","Parameters","--","--","--","--","--","num_classes",":","int","Number","of","semantics","classes","to","predict","over","--","size","of","softmax","classifier","that","run","for","each","point","input_channels",":","int","=","6","Number","of","input","channels","in","the","feature","descriptor","for","each","point",".","If","the","point","cloud","is","Nx9",",","this","value","should","be","6","as","in","an","Nx9","point","cloud",",","3","of","the","channels","are","xyz",",","and","6","are","feature","descriptors","use_xyz",":","bool","=","True","Whether","or","not","to","use","the","xyz","position","of","a","point","as","a","feature"]
docstr: Unpack input data from the dataloader and perform necessary pre-processing steps.
  Parameters:
      input: a dictionary that contains the data itself and its metadata information.
  Sets:
      self.input:
          x -- Features [B, C, N]
          pos -- Points [B, N, 3]
docstr_tok: ["Unpack","input","data","from","the","dataloader","and","perform","necessary","pre-processing","steps",".","Parameters",":","input",":","a","dictionary","that","contains","the","data","itself","and","its","metadata","information",".","Sets",":","self.input",":","x","--","Features","[","B",",","C",",","N","]","pos","--","Points","[","B",",","N",",","3","]"]
code: def set_input(self, data, device):
      """Unpack input data from the dataloader and perform necessary pre-processing steps.
      Parameters:
          input: a dictionary that contains the data itself and its metadata information.
      Sets:
          self.input:
              x -- Features [B, C, N]
              pos -- Points [B, N, 3]
      """
      assert len(data.pos.shape) == 3
      data = data.to(device)
      if data.x is not None:
          x = data.x.transpose(1, 2).contiguous()
      else:
          x = None
      self.input = Data(x=x, pos=data.pos)
      if data.y is not None:
          self.labels = torch.flatten(data.y).long()  # [B * N]
      else:
          self.labels = None
      self.batch_idx = (
          torch.arange(0, data.pos.shape[0])
          .view(-1, 1)
          .repeat(1, data.pos.shape[1])
          .view(-1)
      )
      if self._use_category:
          self.category = data.category
  
file_path: torch-points3d/torch_points3d/models/segmentation/pointnet2.py
imports: import torch
  import torch.nn.functional
  from torch_geometric.data import Data
  import logging
  import copy
  from torch_points3d.modules.pointnet2 import *
  from torch_points3d.core.base_conv.dense import DenseFPModule
  from torch_points3d.models.base_architectures import UnetBasedModel
  from torch_points3d.core.common_modules.dense_modules import Conv1D
  from torch_points3d.core.common_modules.base_modules import Seq
  from .base import Segmentation_MP
  from torch_points3d.datasets.segmentation import IGNORE_LABEL
label_desc: Use image node_id 2 for calling the PointNet2_D obj's underlying member method code with example usage: obj.set_input(data, device) without return types