class_docstr: Semantic segmentation dataset for `MS COCO`_.
  
  Semantic segmentations are generated from panoptic segmentations
  as done in the `official toolkit`_.
  
  .. _`MS COCO`: http://cocodataset.org/#home
  
  .. _`official toolkit`: https://github.com/cocodataset/panopticapi/
      blob/master/converters/panoptic2semantic_segmentation.py
  
  Args:
      data_dir (string): Path to the root of the training data. If this is
          :obj:`auto`, this class will automatically download data for you
          under :obj:`$CHAINER_DATASET_ROOT/pfnet/chainercv/coco`.
      split ({'train', 'val'}): Select a split of the dataset.
  
  This dataset returns the following data.
  
  .. csv-table::
      :header: name, shape, dtype, format
  
      :obj:`img`, ":math:`(3, H, W)`", :obj:`float32`,         "RGB, :math:`[0, 255]`"
      :obj:`label`, ":math:`(H, W)`", :obj:`int32`,         ":math:`[-1, \#class - 1]`"
class_docstr_tok: ["Semantic","segmentation","dataset","for","`","MS","COCO","`","_",".","Semantic","segmentations","are","generated","from","panoptic","segmentations","as","done","in","the","`","official","toolkit","`","_",".","..","_","`","MS","COCO","`",":","http",":","\/\/cocodataset.org\/","#","home","..","_","`","official","toolkit","`",":","https",":","\/\/github.com\/cocodataset\/panopticapi\/","blob\/master\/converters\/panoptic2semantic_segmentation.py","Args",":","data_dir","(","string",")",":","Path","to","the","root","of","the","training","data",".","If","this","is",":","obj",":","`","auto","`",",","this","class","will","automatically","download","data","for","you","under",":","obj",":","`","$","CHAINER_DATASET_ROOT\/pfnet\/chainercv\/coco","`",".","split","(","{","'train","'",",","'val","'","}",")",":","Select","a","split","of","the","dataset",".","This","dataset","returns","the","following","data",".","..","csv-table",":",":",":","header",":","name",",","shape",",","dtype",",","format",":","obj",":","`","img","`",",","``",":","math",":","`","(","3",",","H",",","W",")","`","``",",",":","obj",":","`","float32","`",",","``","RGB",",",":","math",":","`","[","0",",","255","]","`","``",":","obj",":","`","label","`",",","``",":","math",":","`","(","H",",","W",")","`","``",",",":","obj",":","`","int32","`",",","``",":","math",":","`","[","-1",",","\\","#","class","-","1","]","`","``"]
code: def _get_image(self, i):
      img_path = os.path.join(self.img_root, self.img_paths[i])
      img = utils.read_image(img_path, dtype=np.float32, color=True)
      return img
  
file_path: chainercv/chainercv/datasets/coco/coco_semantic_segmentation_dataset.py
imports: import json
  import numpy
  import os
  from chainercv.chainer_experimental.datasets.sliceable import GetterDataset
  from chainercv.datasets.coco.coco_utils import get_coco
  from chainercv import utils
label_desc: Use image node_id 3 for calling the COCOSemanticSegmentationDataset obj's underlying member method code with example usage: obj._get_image(i) and returns: img