text: import tensorflow_hub as hub
  import numpy as np
  import os
  import cv2
  import zipfile
  import telenet.dataset_data as tdsda
  from telenet.utils import create_bbox_depth_mask
  from telenet.config import get as tn_config
  from tqdm import tqdm
  
  VRD_PATH = tn_config('paths.vrd')
  tdsda.load_names('vrd-names.json')
  
  print('Converting semantic vectors...')
  mdl_word2vec = hub.load("https://tfhub.dev/google/Wiki-words-250/2")
  np.save(tdsda.path('vrd-semvecs.npy'), mdl_word2vec(tdsda.CLASS_NAMES))
  
  print('Loading splits...')
  vrd_train = tdsda.load_json_xz('vrd-train')
  vrd_test = tdsda.load_json_xz('vrd-test')
  
  zfdepth = zipfile.ZipFile(os.path.join(VRD_PATH, 'vrd-depth.zip'), 'r')
  
  def cnv_bb(obj):
  	x,y,w,h = obj['bb']
  	return [x,x+w,y,y+h]
  
  def convert_split(vrd_split, splitname):
  	with zipfile.ZipFile(tdsda.path(f'vrd-mask-{splitname}.zip'), 'w', compression=zipfile.ZIP_DEFLATED) as zfo:
  		for img in tqdm(vrd_split):
  			depth_img = cv2.imdecode(np.frombuffer(zfdepth.read(f'{splitname}/{img["id"]}.png'), np.uint8), cv2.IMREAD_ANYDEPTH).astype('float')
  			mmax = np.max(depth_img)
  			mmin = np.min(depth_img)
  			depth_img = 1. - (depth_img - mmin) / (mmax - mmin)
  			depth_img = np.expand_dims(depth_img, 2)
  			masks = create_bbox_depth_mask(depth_img, *(cnv_bb(obj) for obj in img['objs']))
  			with zfo.open(f'{img["id"]}.npy','w') as f:
  				np.save(f, masks)
  
  convert_split(vrd_train, 'train')
  convert_split(vrd_test, 'test')
  