class_docstr: Generates semantic training target only for Panoptic-DeepLab (no instance).
  Annotation is assumed to have Cityscapes format.
  
  Args:
      ignore_index (int): The ignore label for semantic segmentation.
      rgb2id (function): Function, panoptic label is encoded in a colored image, this function convert color to the
          corresponding panoptic label.
class_docstr_tok: ["Generates","semantic","training","target","only","for","Panoptic-DeepLab","(","no","instance",")",".","Annotation","is","assumed","to","have","Cityscapes","format",".","Args",":","ignore_index","(","int",")",":","The","ignore","label","for","semantic","segmentation",".","rgb2id","(","function",")",":","Function",",","panoptic","label","is","encoded","in","a","colored","image",",","this","function","convert","color","to","the","corresponding","panoptic","label","."]
code: def __init__(self, ignore_index, rgb2id):
      self.ignore_index = ignore_index
      self.rgb2id = rgb2id
  
file_path: PaddleSeg/contrib/PanopticDeepLab/transforms/target_transforms.py
imports: import numpy
label_desc: Use image node_id 1 to create a new SemanticTargetGenerator object from inherited base classes: object with example: obj = SemanticTargetGenerator(ignore_index, rgb2id)