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: Initialize last classification layer of MaskPointHead, conv layers
  are already initialized by ConvModule.
docstr_tok: ["Initialize","last","classification","layer","of","MaskPointHead",",","conv","layers","are","already","initialized","by","ConvModule","."]
code: def init_weights(self):
      """Initialize last classification layer of MaskPointHead, conv layers
      are already initialized by ConvModule."""
      normal_init(self.fc_logits, std=0.001)
  
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 2 for calling the MaskPointHead obj's underlying member method code with example usage: obj.init_weights() without return types