docstr: Compute losses of the head.
  
  Args:
      cls_scores (list[Tensor]): Box scores for each scale level
          Has shape (N, num_anchors * num_classes, H, W)
      bbox_preds (list[Tensor]): Box energies / deltas for each scale
          level with shape (N, num_anchors * 4, H, W)
      gt_bboxes (list[Tensor]): Ground truth bboxes of each image
          with shape (num_obj, 4).
      gt_labels (list[Tensor]): Ground truth labels of each image
          with shape (num_obj, 4).
      img_metas (list[dict]): Meta information of each image, e.g.,
          image size, scaling factor, etc.
      gt_bboxes_ignore (list[Tensor]): Ignored gt bboxes of each image.
          Default: None.
  
  Returns:
      dict: Loss dict, comprise classification loss regression loss and
          carl loss.
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code: def loss(
      self,
      cls_scores,
      bbox_preds,
      gt_bboxes,
      gt_labels,
      img_metas,
      gt_bboxes_ignore=None,
  ):
      """Compute losses of the head.
  
      Args:
          cls_scores (list[Tensor]): Box scores for each scale level
              Has shape (N, num_anchors * num_classes, H, W)
          bbox_preds (list[Tensor]): Box energies / deltas for each scale
              level with shape (N, num_anchors * 4, H, W)
          gt_bboxes (list[Tensor]): Ground truth bboxes of each image
              with shape (num_obj, 4).
          gt_labels (list[Tensor]): Ground truth labels of each image
              with shape (num_obj, 4).
          img_metas (list[dict]): Meta information of each image, e.g.,
              image size, scaling factor, etc.
          gt_bboxes_ignore (list[Tensor]): Ignored gt bboxes of each image.
              Default: None.
  
      Returns:
          dict: Loss dict, comprise classification loss regression loss and
              carl loss.
      """
      featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
      assert len(featmap_sizes) == self.anchor_generator.num_levels
  
      device = cls_scores[0].device
  
      anchor_list, valid_flag_list = self.get_anchors(
          featmap_sizes, img_metas, device=device
      )
      cls_reg_targets = self.get_targets(
          anchor_list,
          valid_flag_list,
          gt_bboxes,
          img_metas,
          gt_bboxes_ignore_list=gt_bboxes_ignore,
          gt_labels_list=gt_labels,
          label_channels=1,
          unmap_outputs=False,
          return_sampling_results=True,
      )
      if cls_reg_targets is None:
          return None
      (
          labels_list,
          label_weights_list,
          bbox_targets_list,
          bbox_weights_list,
          num_total_pos,
          num_total_neg,
          sampling_results_list,
      ) = cls_reg_targets
  
      num_images = len(img_metas)
      all_cls_scores = torch.cat(
          [
              s.permute(0, 2, 3, 1).reshape(
                  num_images, -1, self.cls_out_channels
              )
              for s in cls_scores
          ],
          1,
      )
      all_labels = torch.cat(labels_list, -1).view(num_images, -1)
      all_label_weights = torch.cat(label_weights_list, -1).view(
          num_images, -1
      )
      all_bbox_preds = torch.cat(
          [
              b.permute(0, 2, 3, 1).reshape(num_images, -1, 4)
              for b in bbox_preds
          ],
          -2,
      )
      all_bbox_targets = torch.cat(bbox_targets_list, -2).view(
          num_images, -1, 4
      )
      all_bbox_weights = torch.cat(bbox_weights_list, -2).view(
          num_images, -1, 4
      )
  
      # concat all level anchors to a single tensor
      all_anchors = []
      for i in range(num_images):
          all_anchors.append(torch.cat(anchor_list[i]))
  
      isr_cfg = self.train_cfg.get("isr", None)
      all_targets = (
          all_labels.view(-1),
          all_label_weights.view(-1),
          all_bbox_targets.view(-1, 4),
          all_bbox_weights.view(-1, 4),
      )
      # apply ISR-P
      if isr_cfg is not None:
          all_targets = isr_p(
              all_cls_scores.view(-1, all_cls_scores.size(-1)),
              all_bbox_preds.view(-1, 4),
              all_targets,
              torch.cat(all_anchors),
              sampling_results_list,
              loss_cls=CrossEntropyLoss(),
              bbox_coder=self.bbox_coder,
              **self.train_cfg.isr,
              num_class=self.num_classes,
          )
          (
              new_labels,
              new_label_weights,
              new_bbox_targets,
              new_bbox_weights,
          ) = all_targets
          all_labels = new_labels.view(all_labels.shape)
          all_label_weights = new_label_weights.view(
              all_label_weights.shape
          )
          all_bbox_targets = new_bbox_targets.view(
              all_bbox_targets.shape
          )
          all_bbox_weights = new_bbox_weights.view(
              all_bbox_weights.shape
          )
  
      # add CARL loss
      carl_loss_cfg = self.train_cfg.get("carl", None)
      if carl_loss_cfg is not None:
          loss_carl = carl_loss(
              all_cls_scores.view(-1, all_cls_scores.size(-1)),
              all_targets[0],
              all_bbox_preds.view(-1, 4),
              all_targets[2],
              SmoothL1Loss(beta=1.0),
              **self.train_cfg.carl,
              avg_factor=num_total_pos,
              num_class=self.num_classes,
          )
  
      # check NaN and Inf
      assert (
          torch.isfinite(all_cls_scores).all().item()
      ), "classification scores become infinite or NaN!"
      assert (
          torch.isfinite(all_bbox_preds).all().item()
      ), "bbox predications become infinite or NaN!"
  
      losses_cls, losses_bbox = multi_apply(
          self.loss_single,
          all_cls_scores,
          all_bbox_preds,
          all_anchors,
          all_labels,
          all_label_weights,
          all_bbox_targets,
          all_bbox_weights,
          num_total_samples=num_total_pos,
      )
      loss_dict = dict(loss_cls=losses_cls, loss_bbox=losses_bbox)
      if carl_loss_cfg is not None:
          loss_dict.update(loss_carl)
      return loss_dict
  
file_path: insightface/detection/scrfd/mmdet/models/dense_heads/pisa_ssd_head.py
imports: import torch
  from mmdet.core import multi_apply
  from ..builder import HEADS
  from ..losses import CrossEntropyLoss, SmoothL1Loss, carl_loss, isr_p
  from .ssd_head import SSDHead
label_desc: Use image node_id 1 for calling the PISASSDHead obj's underlying member method code with example usage: obj.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, img_metas, gt_bboxes_ignore) and returns: loss_dict, None