class_docstr: YOLACT box head used in https://arxiv.org/abs/1904.02689.
  
  Note that YOLACT head is a light version of RetinaNet head.
  Four differences are described as follows:
  
  1. YOLACT box head has three-times fewer anchors.
  2. YOLACT box head shares the convs for box and cls branches.
  3. YOLACT box head uses OHEM instead of Focal loss.
  4. YOLACT box head predicts a set of mask coefficients for each box.
  
  Args:
      num_classes (int): Number of categories excluding the background
          category.
      in_channels (int): Number of channels in the input feature map.
      anchor_generator (dict): Config dict for anchor generator
      loss_cls (dict): Config of classification loss.
      loss_bbox (dict): Config of localization loss.
      num_head_convs (int): Number of the conv layers shared by
          box and cls branches.
      num_protos (int): Number of the mask coefficients.
      use_ohem (bool): If true, ``loss_single_OHEM`` will be used for
          cls loss calculation. If false, ``loss_single`` will be used.
      conv_cfg (dict): Dictionary to construct and config conv layer.
      norm_cfg (dict): Dictionary to construct and config norm layer.
docstr: A combination of the func:``AnchorHead.loss`` and
  func:``SSDHead.loss``.
  
  When ``self.use_ohem == True``, it functions like ``SSDHead.loss``,
  otherwise, it follows ``AnchorHead.loss``. Besides, it additionally
  returns ``sampling_results``.
  
  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 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 (None | list[Tensor]): Specify which bounding
          boxes can be ignored when computing the loss. Default: None
  
  Returns:
      tuple:
          dict[str, Tensor]: A dictionary of loss components.
          List[:obj:``SamplingResult``]: Sampler results for each image.
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code: def loss(
      self,
      cls_scores,
      bbox_preds,
      gt_bboxes,
      gt_labels,
      img_metas,
      gt_bboxes_ignore=None,
  ):
      """A combination of the func:``AnchorHead.loss`` and
      func:``SSDHead.loss``.
  
      When ``self.use_ohem == True``, it functions like ``SSDHead.loss``,
      otherwise, it follows ``AnchorHead.loss``. Besides, it additionally
      returns ``sampling_results``.
  
      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 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 (None | list[Tensor]): Specify which bounding
              boxes can be ignored when computing the loss. Default: None
  
      Returns:
          tuple:
              dict[str, Tensor]: A dictionary of loss components.
              List[:obj:``SamplingResult``]: Sampler results for each image.
      """
      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,
          unmap_outputs=not self.use_ohem,
          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,
      ) = cls_reg_targets
  
      if self.use_ohem:
          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]))
  
          # 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_OHEM,
              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,
          )
      else:
          num_total_samples = (
              num_total_pos + num_total_neg
              if self.sampling
              else num_total_pos
          )
  
          # anchor number of multi levels
          num_level_anchors = [
              anchors.size(0) for anchors in anchor_list[0]
          ]
          # concat all level anchors and flags to a single tensor
          concat_anchor_list = []
          for i in range(len(anchor_list)):
              concat_anchor_list.append(torch.cat(anchor_list[i]))
          all_anchor_list = images_to_levels(
              concat_anchor_list, num_level_anchors
          )
          losses_cls, losses_bbox = multi_apply(
              self.loss_single,
              cls_scores,
              bbox_preds,
              all_anchor_list,
              labels_list,
              label_weights_list,
              bbox_targets_list,
              bbox_weights_list,
              num_total_samples=num_total_samples,
          )
  
      return (
          dict(loss_cls=losses_cls, loss_bbox=losses_bbox),
          sampling_results,
      )
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import ConvModule, xavier_init
  from mmcv.runner import force_fp32
  from mmdet.core import build_sampler, fast_nms, images_to_levels, multi_apply
  from ..builder import HEADS, build_loss
  from .anchor_head import AnchorHead
label_desc: Use image node_id 5 for calling the YOLACTHead 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: dict,  sampling_results, None