class_docstr: Dataset for ModelNet Classification.
code: def __init__(self, **kwargs):
      super().__init__(
          kwargs["vis_processor"],
          kwargs["text_processor"],
          kwargs["vis_root"],
          [],
      )
  
      self.modalities = kwargs["modalities"]
      # Setting dataset specific properties
      self.npoints = 8192
      self.use_normals = False
      self.num_category = 40
      self.process_data = True
      self.uniform = True
      self.generate_from_raw_data = False
      ann_paths = kwargs["ann_paths"]
  
      assert (
          "pc_root" in kwargs
      ), "Point cloud root needs to be provided to retrieve labels."
      self.pc_root = kwargs["pc_root"]
  
      # Fetching class names and IDs
      self.classnames = [line.rstrip() for line in open(ann_paths[0])]
      self.classes = dict(
          zip(self.classnames, range(len(self.classnames)))
      )
      self.shape_ids = [line.rstrip() for line in open(ann_paths[-1])]
      self.shape_names = [
          "_".join(x.split("_")[0:-1]) for x in self.shape_ids
      ]
  
      # Setting data paths
      self.datapath = [
          (
              self.shape_names[i],
              os.path.join(
                  self.pc_root, self.shape_names[i], self.shape_ids[i]
              )
              + ".txt",
          )
          for i in range(len(self.shape_ids))
      ]
  
      # Saving path settings
      self.save_path = (
          ann_paths[1]
          if self.uniform
          else ann_paths[0].replace("_fps", "")
      )
  
      # Processing or loading data
      self._prepare_data()
  
imports: import os
  from collections import OrderedDict
  import numpy
  from tqdm import tqdm
  import torch
  import copy
  import random
  import pickle
  from PIL import Image
  from lavis.processors.ulip_processors import farthest_point_sample, pc_normalize
  from lavis.datasets.datasets.base_dataset import BaseDataset
label_desc: Use image node_id 1 to create a new ModelNetClassificationDataset object from inherited base classes: BaseDataset, __DisplMixin with example: obj = ModelNetClassificationDataset()