class_docstr: SCL (Semantic Connectivity-aware Learning) framework, which introduces a SC Loss (Semantic Connectivity-aware Loss)
  to improve the quality of segmentation results from the perspective of connectivity. Support multi-class segmentation.
  
  The original article refers to
      Lutao Chu, Yi Liu, Zewu Wu, Shiyu Tang, Guowei Chen, Yuying Hao, Juncai Peng, Zhiliang Yu, Zeyu Chen, Baohua Lai, Haoyi Xiong.
      "PP-HumanSeg: Connectivity-Aware Portrait Segmentation with a Large-Scale Teleconferencing Video Dataset"
      In WACV 2022 workshop
      https://arxiv.org/abs/2112.07146
  
  Running process:
  Step 1. Connected Components Calculation
  Step 2. Connected Components Matching and SC Loss Calculation
docstr: Args:
      ignore_index (int): Specify a pixel value to be ignored in the annotated image and does not contribute to
          the input gradient.When there are pixels that cannot be marked (or difficult to be marked) in the marked
          image, they can be marked as a specific gray value. When calculating the loss value, the pixel corresponding
          to the original image will not be used as the independent variable of the loss function. *Default:``255``*
      max_pred_num_conn (int): Maximum number of predicted connected components. At the beginning of training,
          there will be a large number of connected components, and the calculation is very time-consuming.
          Therefore, it is necessary to limit the maximum number of predicted connected components,
          and the rest will not participate in the calculation.
      use_argmax (bool): Whether to use argmax for logits.
code: def __init__(
      self, ignore_index=255, max_pred_num_conn=10, use_argmax=True
  ):
      """
      Args:
          ignore_index (int): Specify a pixel value to be ignored in the annotated image and does not contribute to
              the input gradient.When there are pixels that cannot be marked (or difficult to be marked) in the marked
              image, they can be marked as a specific gray value. When calculating the loss value, the pixel corresponding
              to the original image will not be used as the independent variable of the loss function. *Default:``255``*
          max_pred_num_conn (int): Maximum number of predicted connected components. At the beginning of training,
              there will be a large number of connected components, and the calculation is very time-consuming.
              Therefore, it is necessary to limit the maximum number of predicted connected components,
              and the rest will not participate in the calculation.
          use_argmax (bool): Whether to use argmax for logits.
      """
      super().__init__()
      self.ignore_index = ignore_index
      self.max_pred_num_conn = max_pred_num_conn
      self.use_argmax = use_argmax
  
file_path: PaddleSeg/paddleseg/models/losses/semantic_connectivity_loss.py
imports: import cv2
  import numpy
  import paddle
  from paddle import nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
label_desc: Use image node_id 1 to create a new SemanticConnectivityLoss object from inherited base classes: nn with example: obj = SemanticConnectivityLoss(ignore_index, max_pred_num_conn, use_argmax)