class_docstr: PKD: General Distillation Framework for Object Detectors via Pearson Correlation Coefficient.
  
  Args:
      loss_weight (float): Weight of loss. Defaults to 1.0.
      resize_stu (bool): If True, we'll down/up sample the features of the
          student model to the spatial size of those of the teacher model if
          their spatial sizes are different. And vice versa. Defaults to
          True.
class_docstr_tok: ["PKD",":","General","Distillation","Framework","for","Object","Detectors","via","Pearson","Correlation","Coefficient",".","Args",":","loss_weight","(","float",")",":","Weight","of","loss",".","Defaults","to","1.0.","resize_stu","(","bool",")",":","If","True",",","we","'ll","down\/up","sample","the","features","of","the","student","model","to","the","spatial","size","of","those","of","the","teacher","model","if","their","spatial","sizes","are","different",".","And","vice","versa",".","Defaults","to","True","."]
code: def __init__(
      self,
      student_channels=256,
      teacher_channels=256,
      normalize=True,
      loss_weight=1.0,
      resize_stu=True,
  ):
      super(PKDFeatureLoss, self).__init__()
      self.normalize = normalize
      self.loss_weight = loss_weight
      self.resize_stu = resize_stu
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr
  from ppdet.core.workspace import register
  from ppdet.modeling import ops
  from ppdet.modeling.losses.iou_loss import GIoULoss
  from ppdet.utils.logger import setup_logger
label_desc: Use image node_id 1 to create a new PKDFeatureLoss object from inherited base classes: nn with example: obj = PKDFeatureLoss(student_channels, teacher_channels, normalize, loss_weight, resize_stu)