class_docstr: Dataset class for the COCO dataset that maps classes to PASCAL VOC classes
  
  Args:
      opts: command-line arguments
class_docstr_tok: ["Dataset","class","for","the","COCO","dataset","that","maps","classes","to","PASCAL","VOC","classes","Args",":","opts",":","command-line","arguments"]
docstr: Generates a mask in PASCAL VOC format
docstr_tok: ["Generates","a","mask","in","PASCAL","VOC","format"]
code: def _gen_seg_mask(self, target, h: int, w: int) -> np.ndarray:
      """Generates a mask in PASCAL VOC format"""
      mask = np.zeros((h, w), dtype=np.uint8)
      coco_mask = self.coco_mask
      coco_to_pascal = self.coco_to_pascal_mapping()
      for instance in target:
          rle = coco_mask.frPyObjects(instance["segmentation"], h, w)
          m = coco_mask.decode(rle)
          cat = instance["category_id"]
          if cat in coco_to_pascal:
              c = coco_to_pascal.index(cat)
          else:
              continue
          if len(m.shape) < 3:
              mask[:, :] += (mask == 0) * (m * c)
          else:
              mask[:, :] += (mask == 0) * (
                  ((np.sum(m, axis=2)) > 0) * c
              ).astype(np.uint8)
      return mask
  
file_path: ml-cvnets/data/datasets/segmentation/coco_segmentation.py
imports: import argparse
  import os
  from typing import List, Mapping, Optional, Tuple, Union
  import numpy
  from pycocotools import mask
  from pycocotools.coco import COCO
  from torch import Tensor
  from data.datasets import DATASET_REGISTRY
  from data.datasets.segmentation.base_segmentation import BaseImageSegmentationDataset
label_desc: Use image node_id 3 for calling the COCOSegmentationDataset obj's underlying member method code with example usage: obj._gen_seg_mask(target, h, w) and returns: mask