class_docstr: This code is based on
  https://github.com/fcjian/TOOD/blob/master/mmdet/models/dense_heads/tood_head.py
class_docstr_tok: ["This","code","is","based","on","https",":","\/\/github.com\/fcjian\/TOOD\/blob\/master\/mmdet\/models\/dense_heads\/tood_head.py"]
code: def get_loss(self, head_outs, gt_meta):
      (
          pred_scores,
          pred_bboxes,
          anchors,
          num_anchors_list,
          stride_tensor,
      ) = head_outs
      gt_labels = gt_meta["gt_class"]
      gt_bboxes = gt_meta["gt_bbox"]
      pad_gt_mask = gt_meta["pad_gt_mask"]
      # label assignment
      if gt_meta["epoch_id"] < self.static_assigner_epoch:
          (
              assigned_labels,
              assigned_bboxes,
              assigned_scores,
          ) = self.static_assigner(
              anchors,
              num_anchors_list,
              gt_labels,
              gt_bboxes,
              pad_gt_mask,
              bg_index=self.num_classes,
          )
          alpha_l = 0.25
      else:
          (
              assigned_labels,
              assigned_bboxes,
              assigned_scores,
          ) = self.assigner(
              pred_scores.detach(),
              pred_bboxes.detach() * stride_tensor,
              bbox_center(anchors),
              num_anchors_list,
              gt_labels,
              gt_bboxes,
              pad_gt_mask,
              bg_index=self.num_classes,
          )
          alpha_l = -1
  
      # rescale bbox
      assigned_bboxes /= stride_tensor
      # classification loss
      loss_cls = self._focal_loss(
          pred_scores, assigned_scores, alpha=alpha_l
      )
      # select positive samples mask
      mask_positive = assigned_labels != self.num_classes
      num_pos = mask_positive.astype(paddle.float32).sum()
      # bbox regression loss
      if num_pos > 0:
          bbox_mask = mask_positive.unsqueeze(-1).tile([1, 1, 4])
          pred_bboxes_pos = paddle.masked_select(
              pred_bboxes, bbox_mask
          ).reshape([-1, 4])
          assigned_bboxes_pos = paddle.masked_select(
              assigned_bboxes, bbox_mask
          ).reshape([-1, 4])
          bbox_weight = paddle.masked_select(
              assigned_scores.sum(-1), mask_positive
          ).unsqueeze(-1)
          # iou loss
          loss_iou = (
              self.giou_loss(pred_bboxes_pos, assigned_bboxes_pos)
              * bbox_weight
          )
          loss_iou = loss_iou.sum() / bbox_weight.sum()
          # l1 loss
          loss_l1 = F.l1_loss(pred_bboxes_pos, assigned_bboxes_pos)
      else:
          loss_iou = paddle.zeros([1])
          loss_l1 = paddle.zeros([1])
  
      loss_cls /= assigned_scores.sum().clip(min=1)
      loss = (
          self.loss_weight["class"] * loss_cls
          + self.loss_weight["iou"] * loss_iou
      )
  
      return {
          "loss": loss,
          "loss_class": loss_cls,
          "loss_iou": loss_iou,
          "loss_l1": loss_l1,
      }
  
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 paddle import ParamAttr
  from paddle.nn.initializer import Constant
  from ppdet.core.workspace import register
  from ..initializer import normal_, constant_, bias_init_with_prob
  from ppdet.modeling.bbox_utils import bbox_center, batch_distance2bbox
  from ..losses import GIoULoss
  from ppdet.modeling.layers import ConvNormLayer
  from ppdet.modeling.ops import get_static_shape
  from ppdet.modeling.assigners.utils import generate_anchors_for_grid_cell
label_desc: Use image node_id 7 for calling the TOODHead obj's underlying member method code with example usage: obj.get_loss(head_outs, gt_meta) and returns: dict