code: def setup(self, bottom, top):
          
          # config
          params = eval(self.param_str)
          self.nyud_dir = params['nyud_dir']
          self.split = params['split']
          self.tops = params['tops']
          self.random = params.get('randomize', True)
          self.seed = params.get('seed', None)
  
          # store top data for reshape + forward
          self.data = {}
  
          # means
          self.mean_bgr = np.array((116.190, 97.203, 92.318), dtype=np.float32)
          self.mean_hha = np.array((132.431, 94.076, 118.477), dtype=np.float32)
          self.mean_logd = np.array((7.844,), dtype=np.float32)
  
          # tops: check configuration
          if len(top) != len(self.tops):
              raise Exception("Need to define {} tops for all outputs.")
          # data layers have no bottoms
          if len(bottom) != 0:
              raise Exception("Do not define a bottom.")
  
          # load indices for images and labels
          split_f  = '{}/{}.txt'.format(self.nyud_dir, self.split)
          self.indices = open(split_f, 'r').read().splitlines()
          self.idx = 0
  
          # make eval deterministic
          if 'train' not in self.split:
              self.random = False
  
          # randomization: seed and pick
          if self.random:
              random.seed(self.seed)
              self.idx = random.randint(0, len(self.indices)-1)
docs: Setup data layer according to parameters:
  
          - nyud_dir: path to NYUDv2 dir
          - split: train / val / test
          - tops: list of tops to output from {color, depth, hha, label}
          - randomize: load in random order (default: True)
          - seed: seed for randomization (default: None / current time)
  
          for NYUDv2 semantic segmentation.
  
          example: params = dict(nyud_dir="/path/to/NYUDVOC2011", split="val",
                                 tops=['color', 'hha', 'label'])