class_docstr: The SemanticFPN-PointRend implementation based on PaddlePaddle.
  
  The original article refers to
  Kirillov A, Wu Y, He K, et al. "PointRend: Image Segmentation As Rendering."
  (https://arxiv.org/abs/1912.08193).
  
  Args:
      num_classes (int): The unique number of target classes.
      backbone (Paddle.nn.Layer): Backbone network, currently support Resnet50/101.
      backbone_indices (tuple, optional): Four values in the tuple indicate the indices of output of backbone.
      fpn_inplanes (list, optional): Input channels list(the feature channels from backbone) for lateral_conv constraction in FPN. Default: [256, 512, 1024, 2048].
      fpn_outplanes (int, optional): The output channels in FPN. Default: 256.
      point_num_fcs (int, optional): Number of fc layers in the head in PointHead. Default: 3.
      point_in_channels (list, optional): input channels of fc block in PointHead. Default: [256].
      point_out_channels (int, optional): Fc block's output channels in PointHead. Default: 256.
      point_in_index (list, optional): The indexs of input features to use in PointHead. Default: [0].
      point_num_points (int, optional): The number of point in training mode in PointHead. Default: 2048.
      point_oversample_ratio (int, optional): The sample ratio of points when in training mode in PointHead.
          sampled_point = num_points * oversample_ratio. Default: 3.
      point_importance_sample_ratio (float, optional): The importance sample ratio for compute num_uncertain_points in PointHead. Default: 0.75.
      point_scale_factor(int, optinal): The scale factor of F.interpolate in refine seg logits stage when in inference in PointHead. Default: 2.
      point_subdivision_steps(int, optional): Then refine steps in refine seg logits stage when in inference in PointHead. Default: 2.
      point_subdivision_num_points(int, optional): The points number for refine seg logits when in inference in PointHead. Default: 8196.
      point_dropout_ratio(float, optional): If the dropout_ratio >0, to use Dropout before output and the p of dropout is dropout_ratio in PointHead. Default: 0.1.
      point_coarse_pred_each_layer(bool, optional): Whether concatenate coarse feature with
          the output of each fc layer in PointHead. Default: True.
      point_conv_cfg(str): The config of Conv in PointHead. Default: 'Conv1D'.
      point_input_transform(str): The features transform method of inputs in PointHead.
          it can be found in function '_transform_inputs'. Defalut: 'multiple_select'.
      PFN_feature_strides(list): The strides for input feature maps and all strides suppose to be power of 2 in FPNHead. The first
          one is of largest resolution. Default: [4, 8, 16, 32].
      PFN_in_channels(list): The input feature's channels list in FPNHead. Default: [256, 256, 256, 256].
      PFN_channels(int,optional): The output channels of scale_head's Conv before Upsample block in FPNHead. Default: 128.
      PFN_in_index(list): The indexs of input features to use. it's shape should keep with in_channels in FPNHead. Default: [0, 1, 2, 3].
      PFN_dropout_ratio(float,optional): If the dropout_ratio >0, to use Dropout before output and the p of dropout is dropout_ratio in FPNHead. Default: 0.1.
      PFN_conv_cfg(str): The config of Conv. Default: 'Conv2D'.
      PFN_input_transform(str): The features transform method of inputs. it can be found in function '_transform_inputs' in FPNHead. Defalut: 'multiple_select'.
      align_corners (bool, optional): An argument of F.interpolate. It should be set to False when the feature size is even,
          e.g. 1024x512, otherwise it is True, e.g. 769x769. Default: False.
      pretrained (str, optional): The path or url of pretrained model. Default: None.
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code: def init_weight(self):
      if self.pretrained is not None:
          utils.load_entire_model(self, self.pretrained)
  
imports: import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from paddleseg.utils import utils
label_desc: Use image node_id 3 for calling the PointRend obj's underlying member method code with example usage: obj.init_weight() without return types