class_docstr: A mask point head use in PointRend.
  
  ``MaskPointHead`` use shared multi-layer perceptron (equivalent to
  nn.Conv1d) to predict the logit of input points. The fine-grained feature
  and coarse feature will be concatenate together for predication.
  
  Args:
      num_fcs (int): Number of fc layers in the head. Defaults to 3.
      in_channels (int): Number of input channels. Defaults to 256.
      fc_channels (int): Number of fc channels. Defaults to 256.
      num_classes (int): Number of classes for logits. Defaults to 80.
      class_agnostic (bool): Whether use class agnostic classification.
          If so, the output channels of logits will be 1. Defaults to False.
      coarse_pred_each_layer (bool): Whether concatenate coarse feature with
          the output of each fc layer. Defaults to True.
      conv_cfg (:obj:`ConfigDict` or dict): Dictionary to construct
          and config conv layer. Defaults to dict(type='Conv1d')).
      norm_cfg (:obj:`ConfigDict` or dict, optional): Dictionary to construct
          and config norm layer. Defaults to None.
      loss_point (:obj:`ConfigDict` or dict): Dictionary to construct and
          config loss layer of point head. Defaults to
          dict(type='CrossEntropyLoss', use_mask=True, loss_weight=1.0).
      init_cfg (:obj:`ConfigDict` or dict or list[:obj:`ConfigDict` or             dict], optional): Initialization config dict.
docstr: Calculate loss for MaskPointHead.
  
  Args:
      point_pred (Tensor): Point predication result, shape
          (num_rois, num_classes, num_points).
      rel_roi_points (Tensor): Points coordinates relative to RoI, shape
          (num_rois, num_points, 2).
       sampling_results (:obj:`SamplingResult`): Sampling result after
          sampling and assignment.
      batch_gt_instances (list[:obj:`InstanceData`]): Batch of
          gt_instance. It usually includes ``bboxes``, ``labels``, and
          ``masks`` attributes.
      cfg (obj:`ConfigDict` or dict): Training cfg.
  
  Returns:
      dict: a dictionary of point loss and point target.
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code: def loss_and_target(
      self,
      point_pred: Tensor,
      rel_roi_points: Tensor,
      sampling_results: List[SamplingResult],
      batch_gt_instances: InstanceList,
      cfg: ConfigType,
  ) -> dict:
      """Calculate loss for MaskPointHead.
  
      Args:
          point_pred (Tensor): Point predication result, shape
              (num_rois, num_classes, num_points).
          rel_roi_points (Tensor): Points coordinates relative to RoI, shape
              (num_rois, num_points, 2).
           sampling_results (:obj:`SamplingResult`): Sampling result after
              sampling and assignment.
          batch_gt_instances (list[:obj:`InstanceData`]): Batch of
              gt_instance. It usually includes ``bboxes``, ``labels``, and
              ``masks`` attributes.
          cfg (obj:`ConfigDict` or dict): Training cfg.
  
      Returns:
          dict: a dictionary of point loss and point target.
      """
      rois = bbox2roi([res.pos_bboxes for res in sampling_results])
      pos_labels = torch.cat(
          [res.pos_gt_labels for res in sampling_results]
      )
  
      point_target = self.get_targets(
          rois,
          rel_roi_points,
          sampling_results,
          batch_gt_instances,
          cfg,
      )
      if self.class_agnostic:
          loss_point = self.loss_point(
              point_pred, point_target, torch.zeros_like(pos_labels)
          )
      else:
          loss_point = self.loss_point(
              point_pred, point_target, pos_labels
          )
  
      return dict(loss_point=loss_point, point_target=point_target)
  
imports: from typing import List, Tuple
  import torch
  import torch.nn
  from mmcv.cnn import ConvModule
  from mmcv.ops import point_sample, rel_roi_point_to_rel_img_point
  from mmengine.model import BaseModule
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.models.task_modules.samplers import SamplingResult
  from mmdet.models.utils import get_uncertain_point_coords_with_randomness, get_uncertainty
  from mmdet.registry import MODELS
  from mmdet.structures.bbox import bbox2roi
  from mmdet.utils import ConfigType, InstanceList, MultiConfig, OptConfigType
label_desc: Use image node_id 5 for calling the MaskPointHead obj's underlying member method code with example usage: obj.loss_and_target(point_pred, rel_roi_points, sampling_results, batch_gt_instances, cfg) and returns: dict