docstr: Calculate the loss based on the features extracted by the mask head.
  
  Args:
      mask_preds (Tensor): Predicted foreground masks, has shape
          (num_pos, num_classes, h, w).
      sampling_results (List[obj:SamplingResult]): Assign results of
          all images in a batch after sampling.
      batch_gt_instances (list[:obj:`InstanceData`]): Batch of
          gt_instance. It usually includes ``bboxes``, ``labels``, and
          ``masks`` attributes.
      rcnn_train_cfg (obj:ConfigDict): `train_cfg` of RCNN.
  
  Returns:
      dict: A dictionary of loss and targets components.
docstr_tok: ["Calculate","the","loss","based","on","the","features","extracted","by","the","mask","head",".","Args",":","mask_preds","(","Tensor",")",":","Predicted","foreground","masks",",","has","shape","(","num_pos",",","num_classes",",","h",",","w",")",".","sampling_results","(","List","[","obj",":","SamplingResult","]",")",":","Assign","results","of","all","images","in","a","batch","after","sampling",".","batch_gt_instances","(","list","[",":","obj",":","`","InstanceData","`","]",")",":","Batch","of","gt_instance",".","It","usually","includes","``","bboxes","``",",","``","labels","``",",","and","``","masks","``","attributes",".","rcnn_train_cfg","(","obj",":","ConfigDict",")",":","`","train_cfg","`","of","RCNN",".","Returns",":","dict",":","A","dictionary","of","loss","and","targets","components","."]
code: def loss_and_target(
      self,
      mask_preds: Tensor,
      sampling_results: List[SamplingResult],
      batch_gt_instances: InstanceList,
      rcnn_train_cfg: ConfigDict,
  ) -> dict:
      """Calculate the loss based on the features extracted by the mask head.
  
      Args:
          mask_preds (Tensor): Predicted foreground masks, has shape
              (num_pos, num_classes, h, w).
          sampling_results (List[obj:SamplingResult]): Assign results of
              all images in a batch after sampling.
          batch_gt_instances (list[:obj:`InstanceData`]): Batch of
              gt_instance. It usually includes ``bboxes``, ``labels``, and
              ``masks`` attributes.
          rcnn_train_cfg (obj:ConfigDict): `train_cfg` of RCNN.
  
      Returns:
          dict: A dictionary of loss and targets components.
      """
      mask_targets = self.get_targets(
          sampling_results=sampling_results,
          batch_gt_instances=batch_gt_instances,
          rcnn_train_cfg=rcnn_train_cfg,
      )
  
      pos_labels = torch.cat(
          [res.pos_gt_labels for res in sampling_results]
      )
  
      loss = dict()
      if mask_preds.size(0) == 0:
          loss_mask = mask_preds.sum()
      else:
          if self.class_agnostic:
              loss_mask = self.loss_mask(
                  mask_preds, mask_targets, torch.zeros_like(pos_labels)
              )
          else:
              loss_mask = self.loss_mask(
                  mask_preds, mask_targets, pos_labels
              )
      loss["loss_mask"] = loss_mask
      # TODO: which algorithm requires mask_targets?
      return dict(loss_mask=loss, mask_targets=mask_targets)
  
imports: from typing import List, Tuple
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import ConvModule, build_conv_layer, build_upsample_layer
  from mmcv.ops.carafe import CARAFEPack
  from mmengine.config import ConfigDict
  from mmengine.model import BaseModule, ModuleList
  from mmengine.structures import InstanceData
  from torch import Tensor
  from torch.nn.modules.utils import _pair
  from mmdet.models.task_modules.samplers import SamplingResult
  from mmdet.models.utils import empty_instances
  from mmdet.registry import MODELS
  from mmdet.structures.mask import mask_target
  from mmdet.utils import ConfigType, InstanceList, OptConfigType, OptMultiConfig
label_desc: Use image node_id 5 for calling the FCNMaskHead obj's underlying member method code with example usage: obj.loss_and_target(mask_preds, sampling_results, batch_gt_instances, rcnn_train_cfg) and returns: dict