class_docstr: Args:
      in_channels (int): the channel number of input to CenterNetHead.
      num_classes (int): the number of classes, 1 (MOT17 dataset) by default.
      head_planes (int): the channel number in all head, 256 by default.
      task (str): the type of task for regression, 'tracking' by default.
      loss_weight (dict): the weight of each loss.
      add_ltrb_amodal (bool): whether to add ltrb_amodal branch, False by default.
class_docstr_tok: ["Args",":","in_channels","(","int",")",":","the","channel","number","of","input","to","CenterNetHead",".","num_classes","(","int",")",":","the","number","of","classes",",","1","(","MOT17","dataset",")","by","default",".","head_planes","(","int",")",":","the","channel","number","in","all","head",",","256","by","default",".","task","(","str",")",":","the","type","of","task","for","regression",",","'tracking","'","by","default",".","loss_weight","(","dict",")",":","the","weight","of","each","loss",".","add_ltrb_amodal","(","bool",")",":","whether","to","add","ltrb_amodal","branch",",","False","by","default","."]
code: def __init__(
      self,
      in_channels,
      num_classes=1,
      head_planes=256,
      task="tracking",
      loss_weight={
          "tracking": 1.0,
          "ltrb_amodal": 0.1,
      },
      add_ltrb_amodal=True,
  ):
      super(CenterTrackHead, self).__init__()
      self.task = task
      self.loss_weight = loss_weight
      self.add_ltrb_amodal = add_ltrb_amodal
  
      # tracking head
      self.tracking = nn.Sequential(
          ConvLayer(
              in_channels,
              head_planes,
              kernel_size=3,
              padding=1,
              bias=True,
          ),
          nn.ReLU(),
          ConvLayer(
              head_planes,
              2,
              kernel_size=1,
              stride=1,
              padding=0,
              bias=True,
          ),
      )
  
      # ltrb_amodal head
      if self.add_ltrb_amodal and "ltrb_amodal" in self.loss_weight:
          self.ltrb_amodal = nn.Sequential(
              ConvLayer(
                  in_channels,
                  head_planes,
                  kernel_size=3,
                  padding=1,
                  bias=True,
              ),
              nn.ReLU(),
              ConvLayer(
                  head_planes,
                  4,
                  kernel_size=1,
                  stride=1,
                  padding=0,
                  bias=True,
              ),
          )
  
file_path: PaddleDetection/ppdet/modeling/heads/centertrack_head.py
imports: import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register
  from .centernet_head import ConvLayer
  from ..keypoint_utils import get_affine_transform
label_desc: Use image node_id 1 to create a new CenterTrackHead object from inherited base classes: nn with example: obj = CenterTrackHead(in_channels, num_classes, head_planes, task, loss_weight, add_ltrb_amodal)