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.
docstr: Calculate the loss based on the features extracted by the detection
  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)
      batch_gt_instances (list[:obj:`InstanceData`]): Batch of
          gt_instance.  It usually includes ``bboxes`` and ``labels``
          attributes.
      batch_img_metas (list[dict]): Meta information of each image, e.g.,
          image size, scaling factor, etc.
      batch_gt_instances_ignore (list[:obj:`InstanceData`], optional):
          Batch of gt_instances_ignore. It includes ``bboxes`` attribute
          data that is ignored during training and testing.
          Defaults to None.
  
  Returns:
      dict[str, Tensor]: A dictionary of loss gmm_assignment.
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code: def loss_by_feat(
      self,
      cls_scores: List[Tensor],
      bbox_preds: List[Tensor],
      iou_preds: List[Tensor],
      batch_gt_instances: InstanceList,
      batch_img_metas: List[dict],
      batch_gt_instances_ignore: OptInstanceList = None,
  ) -> dict:
      """Calculate the loss based on the features extracted by the detection
      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)
          batch_gt_instances (list[:obj:`InstanceData`]): Batch of
              gt_instance.  It usually includes ``bboxes`` and ``labels``
              attributes.
          batch_img_metas (list[dict]): Meta information of each image, e.g.,
              image size, scaling factor, etc.
          batch_gt_instances_ignore (list[:obj:`InstanceData`], optional):
              Batch of gt_instances_ignore. It includes ``bboxes`` attribute
              data that is ignored during training and testing.
              Defaults to None.
  
      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.prior_generator.num_levels
  
      device = cls_scores[0].device
      anchor_list, valid_flag_list = self.get_anchors(
          featmap_sizes, batch_img_metas, device=device
      )
      cls_reg_targets = self.get_targets(
          anchor_list,
          valid_flag_list,
          batch_gt_instances,
          batch_img_metas,
          batch_gt_instances_ignore=batch_gt_instances_ignore,
      )
      (
          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():
          (
              reassign_labels,
              reassign_label_weight,
              reassign_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(reassign_labels, 0).view(-1)
      flatten_anchors = torch.cat(
          [torch.cat(item, 0) for item in anchor_list]
      )
      labels_weight = torch.cat(reassign_label_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(batch_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,
      )
  
imports: from typing import List, Optional, Tuple
  import numpy
  import torch
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import MODELS
  from mmdet.structures.bbox import bbox_overlaps
  from mmdet.utils import ConfigType, InstanceList, OptConfigType, OptInstanceList
  from ..layers import multiclass_nms
  from ..utils import levels_to_images, multi_apply
  from .None import ATSSHead
label_desc: Use image node_id 2 for calling the PAAHead obj's underlying member method code with example usage: obj.loss_by_feat(cls_scores, bbox_preds, iou_preds, batch_gt_instances, batch_img_metas, batch_gt_instances_ignore) and returns: dict