class_docstr: SSDLoss
  
  Args:
      overlap_threshold (float32, optional): IoU threshold for negative bboxes
          and positive bboxes, 0.5 by default.
      neg_pos_ratio (float): The ratio of negative samples / positive samples.
      loc_loss_weight (float): The weight of loc_loss.
      conf_loss_weight (float): The weight of conf_loss.
      prior_box_var (list): Variances corresponding to prior box coord, [0.1,
          0.1, 0.2, 0.2] by default.
class_docstr_tok: ["SSDLoss","Args",":","overlap_threshold","(","float32",",","optional",")",":","IoU","threshold","for","negative","bboxes","and","positive","bboxes",",","0.5","by","default",".","neg_pos_ratio","(","float",")",":","The","ratio","of","negative","samples","\/","positive","samples",".","loc_loss_weight","(","float",")",":","The","weight","of","loc_loss",".","conf_loss_weight","(","float",")",":","The","weight","of","conf_loss",".","prior_box_var","(","list",")",":","Variances","corresponding","to","prior","box","coord",",","[","0.1",",","0.1",",","0.2",",","0.2","]","by","default","."]
code: def __init__(
      self,
      overlap_threshold=0.5,
      neg_pos_ratio=3.0,
      loc_loss_weight=1.0,
      conf_loss_weight=1.0,
      prior_box_var=[0.1, 0.1, 0.2, 0.2],
  ):
      super(SSDLoss, self).__init__()
      self.overlap_threshold = overlap_threshold
      self.neg_pos_ratio = neg_pos_ratio
      self.loc_loss_weight = loc_loss_weight
      self.conf_loss_weight = conf_loss_weight
      self.prior_box_var = [1.0 / a for a in prior_box_var]
  
file_path: PaddleDetection/ppdet/modeling/losses/ssd_loss.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register
  from ..bbox_utils import iou_similarity, bbox2delta
label_desc: Use image node_id 1 to create a new SSDLoss object from inherited base classes: nn with example: obj = SSDLoss(overlap_threshold, neg_pos_ratio, loc_loss_weight, conf_loss_weight, prior_box_var)