class_docstr: Refiner refines the coarse output to full resolution.
  
  Args:
      kernel_size: The convolution kernel_size. Options: [1, 3]. Default: 3.
class_docstr_tok: ["Refiner","refines","the","coarse","output","to","full","resolution",".","Args",":","kernel_size",":","The","convolution","kernel_size",".","Options",":","[","1",",","3","]",".","Default",":","3","."]
code: def __init__(self, kernel_size=3):
      super().__init__()
      if kernel_size not in [1, 3]:
          raise ValueError("kernel_size must be in [1, 3]")
  
      self.kernel_size = kernel_size
  
      channels = [32, 24, 16, 12, 1]
      self.conv1 = layers.ConvBNReLU(
          channels[0] + 4 + 3,
          channels[1],
          kernel_size,
          padding=0,
          bias_attr=False,
      )
      self.conv2 = layers.ConvBNReLU(
          channels[1],
          channels[2],
          kernel_size,
          padding=0,
          bias_attr=False,
      )
      self.conv3 = layers.ConvBNReLU(
          channels[2] + 3,
          channels[3],
          kernel_size,
          padding=0,
          bias_attr=False,
      )
      self.conv4 = nn.Conv2D(
          channels[3],
          channels[4],
          kernel_size,
          padding=0,
          bias_attr=True,
      )
  
imports: from collections import defaultdict
  import time
  import paddle
  import paddle.nn
  import paddle.nn.functional
  import paddleseg
  from paddleseg.models import layers
  from paddleseg import utils
  from paddleseg.cvlibs import manager
  from ppmatting.models.losses import MRSD
label_desc: Use image node_id 1 to create a new Refiner object from inherited base classes: nn with example: obj = Refiner(kernel_size)