class_docstr: This is the PP-HumanSeg14K Dataset.
  
  This dataset was introduced in the work:
  Chu, Lutao, et al. "PP-HumanSeg: Connectivity-Aware Portrait Segmentation with a Large-Scale Teleconferencing Video Dataset." Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2022.
  
  This dataset is divided into training set, validation set and test set. The training set includes 8770 pictures, the validation set includes 2431 pictures, and the test set includes 2482 pictures.
  
  Args:
      dataset_root (str, optional): The dataset directory. Default: None.
      transforms (list, optional): Transforms for image. Default: None.
      mode (str, optional): Which part of dataset to use. It is one of ('train', 'val'). Default: 'train'.
      edge (bool, optional): Whether to compute edge while training. Default: False.
code: def __init__(
      self, dataset_root=None, transforms=None, mode="train", edge=False
  ):
      self.dataset_root = dataset_root
      self.transforms = Compose(transforms)
      mode = mode.lower()
      self.mode = mode
      self.file_list = list()
      self.num_classes = self.NUM_CLASSES
      self.ignore_index = self.IGNORE_INDEX
      self.edge = edge
  
      if mode not in ["train", "val", "test"]:
          raise ValueError(
              "`mode` should be 'train', 'val' or 'test', but got {}.".format(
                  mode
              )
          )
  
      if self.transforms is None:
          raise ValueError("`transforms` is necessary, but it is None.")
  
      if mode == "train":
          file_path = os.path.join(self.dataset_root, "train.txt")
      elif mode == "val":
          file_path = os.path.join(self.dataset_root, "val.txt")
      else:
          file_path = os.path.join(self.dataset_root, "test.txt")
  
      with open(file_path, "r") as f:
          for line in f:
              items = line.strip().split(" ")
              if len(items) != 2:
                  if mode == "train" or mode == "val":
                      raise Exception(
                          "File list format incorrect! It should be"
                          " image_name label_name\\n"
                      )
                  image_path = os.path.join(self.dataset_root, items[0])
                  grt_path = None
              else:
                  image_path = os.path.join(self.dataset_root, items[0])
                  grt_path = os.path.join(self.dataset_root, items[1])
              self.file_list.append([image_path, grt_path])
  
imports: import os
  from .dataset import Dataset
  from paddleseg.cvlibs import manager
  from paddleseg.transforms import Compose
label_desc: Use image node_id 1 to create a new PPHumanSeg14K object from inherited base classes: Dataset with example: obj = PPHumanSeg14K(dataset_root, transforms, mode, edge)