code: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  """
  @Author: Yue Wang
  @Contact: yuewangx@mit.edu
  @File: data.py
  @Time: 2018/10/13 6:21 PM
  
  Modified by 
  @Author: An Tao
  @Contact: ta19@mails.tsinghua.edu.cn
  @Time: 2020/2/27 9:32 PM
  """
  
  
  import os
  import sys
  import glob
  import h5py
  import numpy as np
  import torch
  from torch.utils.data import Dataset
  
  
  def download_modelnet40():
      BASE_DIR = os.path.dirname(os.path.abspath(__file__))
      DATA_DIR = os.path.join(BASE_DIR, 'data')
      if not os.path.exists(DATA_DIR):
          os.mkdir(DATA_DIR)
      if not os.path.exists(os.path.join(DATA_DIR, 'modelnet40_ply_hdf5_2048')):
          www = 'https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip'
          zipfile = os.path.basename(www)
          os.system('wget %s; unzip %s' % (www, zipfile))
          os.system('mv %s %s' % (zipfile[:-4], DATA_DIR))
          os.system('rm %s' % (zipfile))
  
  
  def download_shapenetpart():
      BASE_DIR = os.path.dirname(os.path.abspath(__file__))
      DATA_DIR = os.path.join(BASE_DIR, 'data')
      if not os.path.exists(DATA_DIR):
          os.mkdir(DATA_DIR)
      if not os.path.exists(os.path.join(DATA_DIR, 'shapenet_part_seg_hdf5_data')):
          www = 'https://shapenet.cs.stanford.edu/media/shapenet_part_seg_hdf5_data.zip'
          zipfile = os.path.basename(www)
          os.system('wget %s --no-check-certificate; unzip %s' % (www, zipfile))
          os.system('mv %s %s' % (zipfile[:-4], os.path.join(DATA_DIR, 'shapenet_part_seg_hdf5_data')))
          os.system('rm %s' % (zipfile))
  
  
  def download_S3DIS():
      BASE_DIR = os.path.dirname(os.path.abspath(__file__))
      DATA_DIR = os.path.join(BASE_DIR, 'data')
      if not os.path.exists(DATA_DIR):
          os.mkdir(DATA_DIR)
      if not os.path.exists(os.path.join(DATA_DIR, 'indoor3d_sem_seg_hdf5_data')):
          www = 'https://shapenet.cs.stanford.edu/media/indoor3d_sem_seg_hdf5_data.zip'
          zipfile = os.path.basename(www)
          os.system('wget --no-check-certificate %s; unzip %s' % (www, zipfile))
          os.system('mv %s %s' % (zipfile[:-4], DATA_DIR))
          os.system('rm %s' % (zipfile))
      if not os.path.exists(os.path.join(DATA_DIR, 'Stanford3dDataset_v1.2_Aligned_Version')):
          if not os.path.exists(os.path.join(DATA_DIR, 'Stanford3dDataset_v1.2_Aligned_Version.zip')):
              print('Please download Stanford3dDataset_v1.2_Aligned_Version.zip \
                  from https://goo.gl/forms/4SoGp4KtH1jfRqEj2 and place it under data/')
              sys.exit(0)
          else:
              zippath = os.path.join(DATA_DIR, 'Stanford3dDataset_v1.2_Aligned_Version.zip')
              os.system('unzip %s' % (zippath))
              os.system('rm %s' % (zippath))
  
  
  def load_data_cls(partition):
      download_modelnet40()
      BASE_DIR = os.path.dirname(os.path.abspath(__file__))
      DATA_DIR = os.path.join(BASE_DIR, 'data')
      all_data = []
      all_label = []
      for h5_name in glob.glob(os.path.join(DATA_DIR, 'modelnet40*hdf5_2048', '*%s*.h5'%partition)):
          f = h5py.File(h5_name, 'r+')
          data = f['data'][:].astype('float32')
          label = f['label'][:].astype('int64')
          f.close()
          all_data.append(data)
          all_label.append(label)
      all_data = np.concatenate(all_data, axis=0)
      all_label = np.concatenate(all_label, axis=0)
      return all_data, all_label
  
  
  def load_data_partseg(partition):
      download_shapenetpart()
      BASE_DIR = os.path.dirname(os.path.abspath(__file__))
      DATA_DIR = os.path.join(BASE_DIR, 'data')
      all_data = []
      all_label = []
      all_seg = []
      if partition == 'trainval':
          file = glob.glob(os.path.join(DATA_DIR, 'shapenet*hdf5*', '*train*.h5')) \
                 + glob.glob(os.path.join(DATA_DIR, 'shapenet*hdf5*', '*val*.h5'))
      else:
          file = glob.glob(os.path.join(DATA_DIR, 'shapenet*hdf5*', '*%s*.h5'%partition))
      for h5_name in file:
          f = h5py.File(h5_name, 'r+')
          data = f['data'][:].astype('float32')
          label = f['label'][:].astype('int64')
          seg = f['pid'][:].astype('int64')
          f.close()
          all_data.append(data)
          all_label.append(label)
          all_seg.append(seg)
      all_data = np.concatenate(all_data, axis=0)
      all_label = np.concatenate(all_label, axis=0)
      all_seg = np.concatenate(all_seg, axis=0)
      return all_data, all_label, all_seg
  
  
  def prepare_test_data_semseg():
      BASE_DIR = os.path.dirname(os.path.abspath(__file__))
      DATA_DIR = os.path.join(BASE_DIR, 'data')
      if not os.path.exists(os.path.join(DATA_DIR, 'stanford_indoor3d')):
          os.system('python prepare_data/collect_indoor3d_data.py')
      if not os.path.exists(os.path.join(DATA_DIR, 'indoor3d_sem_seg_hdf5_data_test')):
          os.system('python prepare_data/gen_indoor3d_h5.py')
  
  
  def load_data_semseg(partition, test_area):
      BASE_DIR = os.path.dirname(os.path.abspath(__file__))
      DATA_DIR = os.path.join(BASE_DIR, 'data')
      download_S3DIS()
      prepare_test_data_semseg()
      if partition == 'train':
          data_dir = os.path.join(DATA_DIR, 'indoor3d_sem_seg_hdf5_data')
      else:
          data_dir = os.path.join(DATA_DIR, 'indoor3d_sem_seg_hdf5_data_test')
      with open(os.path.join(data_dir, "all_files.txt")) as f:
          all_files = [line.rstrip() for line in f]
      with open(os.path.join(data_dir, "room_filelist.txt")) as f:
          room_filelist = [line.rstrip() for line in f]
      data_batchlist, label_batchlist = [], []
      for f in all_files:
          file = h5py.File(os.path.join(DATA_DIR, f), 'r+')
          data = file["data"][:]
          label = file["label"][:]
          data_batchlist.append(data)
          label_batchlist.append(label)
      data_batches = np.concatenate(data_batchlist, 0)
      seg_batches = np.concatenate(label_batchlist, 0)
      test_area_name = "Area_" + test_area
      train_idxs, test_idxs = [], []
      for i, room_name in enumerate(room_filelist):
          if test_area_name in room_name:
              test_idxs.append(i)
          else:
              train_idxs.append(i)
      if partition == 'train':
          all_data = data_batches[train_idxs, ...]
          all_seg = seg_batches[train_idxs, ...]
      else:
          all_data = data_batches[test_idxs, ...]
          all_seg = seg_batches[test_idxs, ...]
      return all_data, all_seg
  
  
  def translate_pointcloud(pointcloud):
      xyz1 = np.random.uniform(low=2./3., high=3./2., size=[3])
      xyz2 = np.random.uniform(low=-0.2, high=0.2, size=[3])
         
      translated_pointcloud = np.add(np.multiply(pointcloud, xyz1), xyz2).astype('float32')
      return translated_pointcloud
  
  
  def jitter_pointcloud(pointcloud, sigma=0.01, clip=0.02):
      N, C = pointcloud.shape
      pointcloud += np.clip(sigma * np.random.randn(N, C), -1*clip, clip)
      return pointcloud
  
  
  def rotate_pointcloud(pointcloud):
      theta = np.pi*2 * np.random.uniform()
      rotation_matrix = np.array([[np.cos(theta), -np.sin(theta)],[np.sin(theta), np.cos(theta)]])
      pointcloud[:,[0,2]] = pointcloud[:,[0,2]].dot(rotation_matrix) # random rotation (x,z)
      return pointcloud
  
  
  class ModelNet40(Dataset):
      def __init__(self, num_points, partition='train'):
          self.data, self.label = load_data_cls(partition)
          self.num_points = num_points
          self.partition = partition        
  
      def __getitem__(self, item):
          pointcloud = self.data[item][:self.num_points]
          label = self.label[item]
          if self.partition == 'train':
              pointcloud = translate_pointcloud(pointcloud)
              np.random.shuffle(pointcloud)
          return pointcloud, label
  
      def __len__(self):
          return self.data.shape[0]
  
  
  class ShapeNetPart(Dataset):
      def __init__(self, num_points, partition='train', class_choice=None):
          self.data, self.label, self.seg = load_data_partseg(partition)
          self.cat2id = {'airplane': 0, 'bag': 1, 'cap': 2, 'car': 3, 'chair': 4, 
                         'earphone': 5, 'guitar': 6, 'knife': 7, 'lamp': 8, 'laptop': 9, 
                         'motor': 10, 'mug': 11, 'pistol': 12, 'rocket': 13, 'skateboard': 14, 'table': 15}
          self.seg_num = [4, 2, 2, 4, 4, 3, 3, 2, 4, 2, 6, 2, 3, 3, 3, 3]
          self.index_start = [0, 4, 6, 8, 12, 16, 19, 22, 24, 28, 30, 36, 38, 41, 44, 47]
          self.num_points = num_points
          self.partition = partition        
          self.class_choice = class_choice
  
          if self.class_choice != None:
              id_choice = self.cat2id[self.class_choice]
              indices = (self.label == id_choice).squeeze()
              self.data = self.data[indices]
              self.label = self.label[indices]
              self.seg = self.seg[indices]
              self.seg_num_all = self.seg_num[id_choice]
              self.seg_start_index = self.index_start[id_choice]
          else:
              self.seg_num_all = 50
              self.seg_start_index = 0
  
      def __getitem__(self, item):
          pointcloud = self.data[item][:self.num_points]
          label = self.label[item]
          seg = self.seg[item][:self.num_points]
          if self.partition == 'trainval':
              # pointcloud = translate_pointcloud(pointcloud)
              indices = list(range(pointcloud.shape[0]))
              np.random.shuffle(indices)
              pointcloud = pointcloud[indices]
              seg = seg[indices]
          return pointcloud, label, seg
  
      def __len__(self):
          return self.data.shape[0]
  
  
  class S3DIS(Dataset):
      def __init__(self, num_points=4096, partition='train', test_area='1'):
          self.data, self.seg = load_data_semseg(partition, test_area)
          self.num_points = num_points
          self.partition = partition        
  
      def __getitem__(self, item):
          pointcloud = self.data[item][:self.num_points]
          seg = self.seg[item][:self.num_points]
          if self.partition == 'train':
              indices = list(range(pointcloud.shape[0]))
              np.random.shuffle(indices)
              pointcloud = pointcloud[indices]
              seg = seg[indices]
          seg = torch.LongTensor(seg)
          return pointcloud, seg
  
      def __len__(self):
          return self.data.shape[0]
  
  
  if __name__ == '__main__':
      train = ModelNet40(1024)
      test = ModelNet40(1024, 'test')
      data, label = train[0]
      print(data.shape)
      print(label.shape)
  
      trainval = ShapeNetPart(2048, 'trainval')
      test = ShapeNetPart(2048, 'test')
      data, label, seg = trainval[0]
      print(data.shape)
      print(label.shape)
      print(seg.shape)
  
      train = S3DIS(4096)
      test = S3DIS(4096, 'test')
      data, seg = train[0]
      print(data.shape)
      print(seg.shape)
  
apis: numpy.random.uniform numpy.multiply numpy.random.shuffle numpy.random.randn numpy.cos numpy.sin numpy.concatenate torch.LongTensor