class_docstr: Simplest RoI head, with only two fc layers for classification and
  regression respectively.
class_docstr_tok: ["Simplest","RoI","head",",","with","only","two","fc","layers","for","classification","and","regression","respectively","."]
docstr: Calculate the loss based on the features extracted by the bbox head.
  
  Args:
      cls_score (Tensor): Classification prediction
          results of all class, has shape
          (batch_size * num_proposals_single_image, num_classes)
      bbox_pred (Tensor): Regression prediction results,
          has shape
          (batch_size * num_proposals_single_image, 4), the last
          dimension 4 represents [tl_x, tl_y, br_x, br_y].
      rois (Tensor): RoIs with the shape
          (batch_size * num_proposals_single_image, 5) where the first
          column indicates batch id of each RoI.
      sampling_results (List[obj:SamplingResult]): Assign results of
          all images in a batch after sampling.
      rcnn_train_cfg (obj:ConfigDict): `train_cfg` of RCNN.
      concat (bool): Whether to concatenate the results of all
          the images in a single batch. Defaults to True.
      reduction_override (str, optional): The reduction
          method used to override the original reduction
          method of the loss. Options are "none",
          "mean" and "sum". Defaults to None,
  
  Returns:
      dict: A dictionary of loss and targets components.
          The targets are only used for cascade rcnn.
docstr_tok: ["Calculate","the","loss","based","on","the","features","extracted","by","the","bbox","head",".","Args",":","cls_score","(","Tensor",")",":","Classification","prediction","results","of","all","class",",","has","shape","(","batch_size","*","num_proposals_single_image",",","num_classes",")","bbox_pred","(","Tensor",")",":","Regression","prediction","results",",","has","shape","(","batch_size","*","num_proposals_single_image",",","4",")",",","the","last","dimension","4","represents","[","tl_x",",","tl_y",",","br_x",",","br_y","]",".","rois","(","Tensor",")",":","RoIs","with","the","shape","(","batch_size","*","num_proposals_single_image",",","5",")","where","the","first","column","indicates","batch","id","of","each","RoI",".","sampling_results","(","List","[","obj",":","SamplingResult","]",")",":","Assign","results","of","all","images","in","a","batch","after","sampling",".","rcnn_train_cfg","(","obj",":","ConfigDict",")",":","`","train_cfg","`","of","RCNN",".","concat","(","bool",")",":","Whether","to","concatenate","the","results","of","all","the","images","in","a","single","batch",".","Defaults","to","True",".","reduction_override","(","str",",","optional",")",":","The","reduction","method","used","to","override","the","original","reduction","method","of","the","loss",".","Options","are","``","none","''",",","``","mean","''","and","``","sum","''",".","Defaults","to","None",",","Returns",":","dict",":","A","dictionary","of","loss","and","targets","components",".","The","targets","are","only","used","for","cascade","rcnn","."]
code: def loss_and_target(
      self,
      cls_score: Tensor,
      bbox_pred: Tensor,
      rois: Tensor,
      sampling_results: List[SamplingResult],
      rcnn_train_cfg: ConfigDict,
      concat: bool = True,
      reduction_override: Optional[str] = None,
  ) -> dict:
      """Calculate the loss based on the features extracted by the bbox head.
  
      Args:
          cls_score (Tensor): Classification prediction
              results of all class, has shape
              (batch_size * num_proposals_single_image, num_classes)
          bbox_pred (Tensor): Regression prediction results,
              has shape
              (batch_size * num_proposals_single_image, 4), the last
              dimension 4 represents [tl_x, tl_y, br_x, br_y].
          rois (Tensor): RoIs with the shape
              (batch_size * num_proposals_single_image, 5) where the first
              column indicates batch id of each RoI.
          sampling_results (List[obj:SamplingResult]): Assign results of
              all images in a batch after sampling.
          rcnn_train_cfg (obj:ConfigDict): `train_cfg` of RCNN.
          concat (bool): Whether to concatenate the results of all
              the images in a single batch. Defaults to True.
          reduction_override (str, optional): The reduction
              method used to override the original reduction
              method of the loss. Options are "none",
              "mean" and "sum". Defaults to None,
  
      Returns:
          dict: A dictionary of loss and targets components.
              The targets are only used for cascade rcnn.
      """
  
      cls_reg_targets = self.get_targets(
          sampling_results, rcnn_train_cfg, concat=concat
      )
      losses = self.loss(
          cls_score,
          bbox_pred,
          rois,
          *cls_reg_targets,
          reduction_override=reduction_override,
      )
  
      # cls_reg_targets is only for cascade rcnn
      return dict(loss_bbox=losses, bbox_targets=cls_reg_targets)
  
imports: from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from mmengine.config import ConfigDict
  from mmengine.model import BaseModule
  from mmengine.structures import InstanceData
  from torch import Tensor
  from torch.nn.modules.utils import _pair
  from mmdet.models.layers import multiclass_nms
  from mmdet.models.losses import accuracy
  from mmdet.models.task_modules.samplers import SamplingResult
  from mmdet.models.utils import empty_instances, multi_apply
  from mmdet.registry import MODELS, TASK_UTILS
  from mmdet.structures.bbox import get_box_tensor, scale_boxes
  from mmdet.utils import ConfigType, InstanceList, OptMultiConfig
label_desc: Use image node_id 8 for calling the BBoxHead obj's underlying member method code with example usage: obj.loss_and_target(cls_score, bbox_pred, rois, sampling_results, rcnn_train_cfg, concat, reduction_override) and returns: dict