class_docstr: A self-developed ultra lightweight model from PaddleSeg, is suitable for real-time scene segmentation on web or mobile terminals.
class_docstr_tok: ["A","self-developed","ultra","lightweight","model","from","PaddleSeg",",","is","suitable","for","real-time","scene","segmentation","on","web","or","mobile","terminals","."]
code: def init_weight(self):
      for layer in self.sublayers():
          if isinstance(layer, nn.Conv2D):
              param_init.normal_init(layer.weight, std=0.001)
          elif isinstance(layer, (nn.BatchNorm, nn.SyncBatchNorm)):
              param_init.constant_init(layer.weight, value=1.0)
              param_init.constant_init(layer.bias, value=0.0)
      if self.pretrained is not None:
          utils.load_pretrained_model(self, self.pretrained)
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.models import layers
  from paddleseg.utils import utils
label_desc: Use image node_id 3 for calling the PPHumanSegLite obj's underlying member method code with example usage: obj.init_weight() without return types