class_docstr: Head of PAAAssignment: Probabilistic Anchor Assignment with IoU
  Prediction for Object Detection.
  
  Code is modified from the `official github repo
  <https://github.com/kkhoot/PAA/blob/master/paa_core
  /modeling/rpn/paa/loss.py>`_.
  
  More details can be found in the `paper
  <https://arxiv.org/abs/2007.08103>`_ .
  
  Args:
      topk (int): Select topk samples with smallest loss in
          each level.
      score_voting (bool): Whether to use score voting in post-process.
      covariance_type : String describing the type of covariance parameters
          to be used in :class:`sklearn.mixture.GaussianMixture`.
          It must be one of:
  
          - 'full': each component has its own general covariance matrix
          - 'tied': all components share the same general covariance matrix
          - 'diag': each component has its own diagonal covariance matrix
          - 'spherical': each component has its own single variance
          Default: 'diag'. From 'full' to 'spherical', the gmm fitting
          process is faster yet the performance could be influenced. For most
          cases, 'diag' should be a good choice.
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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)
      iou_preds (list[Tensor]): iou_preds for each scale
          level with shape (N, num_anchors * 1, H, W)
      gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
          shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
      gt_labels (list[Tensor]): class indices corresponding to each box
      img_metas (list[dict]): Meta information of each image, e.g.,
          image size, scaling factor, etc.
      gt_bboxes_ignore (list[Tensor] | None): Specify which bounding
          boxes can be ignored when are computing the loss.
  
  Returns:
      dict[str, Tensor]: A dictionary of loss gmm_assignment.
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code: def loss(
      self,
      cls_scores,
      bbox_preds,
      iou_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)
          iou_preds (list[Tensor]): iou_preds for each scale
              level with shape (N, num_anchors * 1, H, W)
          gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
              shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
          gt_labels (list[Tensor]): class indices corresponding to each box
          img_metas (list[dict]): Meta information of each image, e.g.,
              image size, scaling factor, etc.
          gt_bboxes_ignore (list[Tensor] | None): Specify which bounding
              boxes can be ignored when are computing the loss.
  
      Returns:
          dict[str, Tensor]: A dictionary of loss gmm_assignment.
      """
  
      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
      )
      label_channels = (
          self.cls_out_channels if self.use_sigmoid_cls else 1
      )
      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=label_channels,
      )
      (
          labels,
          labels_weight,
          bboxes_target,
          bboxes_weight,
          pos_inds,
          pos_gt_index,
      ) = cls_reg_targets
      cls_scores = levels_to_images(cls_scores)
      cls_scores = [
          item.reshape(-1, self.cls_out_channels) for item in cls_scores
      ]
      bbox_preds = levels_to_images(bbox_preds)
      bbox_preds = [item.reshape(-1, 4) for item in bbox_preds]
      iou_preds = levels_to_images(iou_preds)
      iou_preds = [item.reshape(-1, 1) for item in iou_preds]
      (pos_losses_list,) = multi_apply(
          self.get_pos_loss,
          anchor_list,
          cls_scores,
          bbox_preds,
          labels,
          labels_weight,
          bboxes_target,
          bboxes_weight,
          pos_inds,
      )
  
      with torch.no_grad():
          labels, label_weights, bbox_weights, num_pos = multi_apply(
              self.paa_reassign,
              pos_losses_list,
              labels,
              labels_weight,
              bboxes_weight,
              pos_inds,
              pos_gt_index,
              anchor_list,
          )
          num_pos = sum(num_pos)
      # convert all tensor list to a flatten tensor
      cls_scores = torch.cat(cls_scores, 0).view(
          -1, cls_scores[0].size(-1)
      )
      bbox_preds = torch.cat(bbox_preds, 0).view(
          -1, bbox_preds[0].size(-1)
      )
      iou_preds = torch.cat(iou_preds, 0).view(
          -1, iou_preds[0].size(-1)
      )
      labels = torch.cat(labels, 0).view(-1)
      flatten_anchors = torch.cat(
          [torch.cat(item, 0) for item in anchor_list]
      )
      labels_weight = torch.cat(labels_weight, 0).view(-1)
      bboxes_target = torch.cat(bboxes_target, 0).view(
          -1, bboxes_target[0].size(-1)
      )
  
      pos_inds_flatten = (
          ((labels >= 0) & (labels < self.num_classes))
          .nonzero()
          .reshape(-1)
      )
  
      losses_cls = self.loss_cls(
          cls_scores,
          labels,
          labels_weight,
          avg_factor=max(num_pos, len(img_metas)),
      )  # avoid num_pos=0
      if num_pos:
          pos_bbox_pred = self.bbox_coder.decode(
              flatten_anchors[pos_inds_flatten],
              bbox_preds[pos_inds_flatten],
          )
          pos_bbox_target = bboxes_target[pos_inds_flatten]
          iou_target = bbox_overlaps(
              pos_bbox_pred.detach(), pos_bbox_target, is_aligned=True
          )
          losses_iou = self.loss_centerness(
              iou_preds[pos_inds_flatten],
              iou_target.unsqueeze(-1),
              avg_factor=num_pos,
          )
          losses_bbox = self.loss_bbox(
              pos_bbox_pred,
              pos_bbox_target,
              iou_target.clamp(min=EPS),
              avg_factor=iou_target.sum(),
          )
      else:
          losses_iou = iou_preds.sum() * 0
          losses_bbox = bbox_preds.sum() * 0
  
      return dict(
          loss_cls=losses_cls,
          loss_bbox=losses_bbox,
          loss_iou=losses_iou,
      )
  
file_path: insightface/detection/scrfd/mmdet/models/dense_heads/paa_head.py
imports: import numpy
  import torch
  from mmcv.runner import force_fp32
  from mmdet.core import multi_apply, multiclass_nms
  from mmdet.core.bbox.iou_calculators import bbox_overlaps
  from mmdet.models import HEADS
  from mmdet.models.dense_heads import ATSSHead
label_desc: Use image node_id 2 for calling the PAAHead obj's underlying member method code with example usage: obj.loss(cls_scores, bbox_preds, iou_preds, gt_bboxes, gt_labels, img_metas, gt_bboxes_ignore) and returns: dict