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. Default: 3.
      in_channels (int): Number of input channels. Default: 256.
      fc_channels (int): Number of fc channels. Default: 256.
      num_classes (int): Number of classes for logits. Default: 80.
      class_agnostic (bool): Whether use class agnostic classification.
          If so, the output channels of logits will be 1. Default: False.
      coarse_pred_each_layer (bool): Whether concatenate coarse feature with
          the output of each fc layer. Default: True.
      conv_cfg (dict | None): Dictionary to construct and config conv layer.
          Default: dict(type='Conv1d'))
      norm_cfg (dict | None): Dictionary to construct and config norm layer.
          Default: None.
      loss_point (dict): Dictionary to construct and config loss layer of
          point head. Default: dict(type='CrossEntropyLoss', use_mask=True,
          loss_weight=1.0).
docstr: Get training target of MaskPointHead for each image.
docstr_tok: ["Get","training","target","of","MaskPointHead","for","each","image","."]
code: def _get_target_single(
      self, rois, rel_roi_points, pos_assigned_gt_inds, gt_masks, cfg
  ):
      """Get training target of MaskPointHead for each image."""
      num_pos = rois.size(0)
      num_points = cfg.num_points
      if num_pos > 0:
          gt_masks_th = gt_masks.to_tensor(
              rois.dtype, rois.device
          ).index_select(0, pos_assigned_gt_inds)
          gt_masks_th = gt_masks_th.unsqueeze(1)
          rel_img_points = rel_roi_point_to_rel_img_point(
              rois, rel_roi_points, gt_masks_th.shape[2:]
          )
          point_targets = point_sample(
              gt_masks_th, rel_img_points
          ).squeeze(1)
      else:
          point_targets = rois.new_zeros((0, num_points))
      return point_targets
  
file_path: insightface/detection/scrfd/mmdet/models/roi_heads/mask_heads/mask_point_head.py
imports: import torch
  import torch.nn
  from mmcv.cnn import ConvModule, normal_init
  from mmcv.ops import point_sample, rel_roi_point_to_rel_img_point
  from mmdet.models.builder import HEADS, build_loss
label_desc: Use image node_id 5 for calling the MaskPointHead obj's underlying member method code with example usage: obj._get_target_single(rois, rel_roi_points, pos_assigned_gt_inds, gt_masks, cfg) and returns: point_targets