class_docstr: A model for 
class_docstr_tok: ["A","model","for"]
code: def __init__(
      self,
      backbone,
      pretrained=None,
      backbone_scale=0.25,
      refine_kernel_size=3,
      if_refine=True,
  ):
      super().__init__()
      if if_refine:
          if backbone_scale > 0.5:
              raise ValueError(
                  "Backbone_scale should not be greater than 1/2, but it is {}".format(
                      backbone_scale
                  )
              )
      else:
          backbone_scale = 1
  
      self.backbone = backbone
      self.backbone_scale = backbone_scale
      self.pretrained = pretrained
      self.if_refine = if_refine
      if if_refine:
          self.refiner = Refiner(kernel_size=refine_kernel_size)
      self.loss_func_dict = None
  
      self.backbone_channels = backbone.feat_channels
      ######################
      ### Decoder part - Glance
      ######################
      self.psp_module = layers.PPModule(
          self.backbone_channels[-1],
          512,
          bin_sizes=(1, 3, 5),
          dim_reduction=False,
          align_corners=False,
      )
      self.psp4 = conv_up_psp(512, 256, 2)
      self.psp3 = conv_up_psp(512, 128, 4)
      self.psp2 = conv_up_psp(512, 64, 8)
      self.psp1 = conv_up_psp(512, 64, 16)
      # stage 5g
      self.decoder5_g = nn.Sequential(
          layers.ConvBNReLU(
              512 + self.backbone_channels[-1], 512, 3, padding=1
          ),
          layers.ConvBNReLU(512, 512, 3, padding=2, dilation=2),
          layers.ConvBNReLU(512, 256, 3, padding=2, dilation=2),
          nn.Upsample(
              scale_factor=2, mode="bilinear", align_corners=False
          ),
      )
      # stage 4g
      self.decoder4_g = nn.Sequential(
          layers.ConvBNReLU(512, 256, 3, padding=1),
          layers.ConvBNReLU(256, 256, 3, padding=1),
          layers.ConvBNReLU(256, 128, 3, padding=1),
          nn.Upsample(
              scale_factor=2, mode="bilinear", align_corners=False
          ),
      )
      # stage 3g
      self.decoder3_g = nn.Sequential(
          layers.ConvBNReLU(256, 128, 3, padding=1),
          layers.ConvBNReLU(128, 128, 3, padding=1),
          layers.ConvBNReLU(128, 64, 3, padding=1),
          nn.Upsample(
              scale_factor=2, mode="bilinear", align_corners=False
          ),
      )
      # stage 2g
      self.decoder2_g = nn.Sequential(
          layers.ConvBNReLU(128, 128, 3, padding=1),
          layers.ConvBNReLU(128, 128, 3, padding=1),
          layers.ConvBNReLU(128, 64, 3, padding=1),
          nn.Upsample(
              scale_factor=2, mode="bilinear", align_corners=False
          ),
      )
      # stage 1g
      self.decoder1_g = nn.Sequential(
          layers.ConvBNReLU(128, 64, 3, padding=1),
          layers.ConvBNReLU(64, 64, 3, padding=1),
          layers.ConvBNReLU(64, 64, 3, padding=1),
          nn.Upsample(
              scale_factor=2, mode="bilinear", align_corners=False
          ),
      )
      # stage 0g
      self.decoder0_g = nn.Sequential(
          layers.ConvBNReLU(64, 64, 3, padding=1),
          layers.ConvBNReLU(64, 64, 3, padding=1),
          nn.Conv2D(64, 3, 3, padding=1),
      )
  
      ##########################
      ### Decoder part - FOCUS
      ##########################
      self.bridge_block = nn.Sequential(
          layers.ConvBNReLU(
              self.backbone_channels[-1], 512, 3, dilation=2, padding=2
          ),
          layers.ConvBNReLU(512, 512, 3, dilation=2, padding=2),
          layers.ConvBNReLU(512, 512, 3, dilation=2, padding=2),
      )
      # stage 5f
      self.decoder5_f = nn.Sequential(
          layers.ConvBNReLU(
              512 + self.backbone_channels[-1], 512, 3, padding=1
          ),
          layers.ConvBNReLU(512, 512, 3, padding=2, dilation=2),
          layers.ConvBNReLU(512, 256, 3, padding=2, dilation=2),
          nn.Upsample(
              scale_factor=2, mode="bilinear", align_corners=False
          ),
      )
      # stage 4f
      self.decoder4_f = nn.Sequential(
          layers.ConvBNReLU(
              256 + self.backbone_channels[-2], 256, 3, padding=1
          ),
          layers.ConvBNReLU(256, 256, 3, padding=1),
          layers.ConvBNReLU(256, 128, 3, padding=1),
          nn.Upsample(
              scale_factor=2, mode="bilinear", align_corners=False
          ),
      )
      # stage 3f
      self.decoder3_f = nn.Sequential(
          layers.ConvBNReLU(
              128 + self.backbone_channels[-3], 128, 3, padding=1
          ),
          layers.ConvBNReLU(128, 128, 3, padding=1),
          layers.ConvBNReLU(128, 64, 3, padding=1),
          nn.Upsample(
              scale_factor=2, mode="bilinear", align_corners=False
          ),
      )
      # stage 2f
      self.decoder2_f = nn.Sequential(
          layers.ConvBNReLU(
              64 + self.backbone_channels[-4], 128, 3, padding=1
          ),
          layers.ConvBNReLU(128, 128, 3, padding=1),
          layers.ConvBNReLU(128, 64, 3, padding=1),
          nn.Upsample(
              scale_factor=2, mode="bilinear", align_corners=False
          ),
      )
      # stage 1f
      self.decoder1_f = nn.Sequential(
          layers.ConvBNReLU(
              64 + self.backbone_channels[-5], 64, 3, padding=1
          ),
          layers.ConvBNReLU(64, 64, 3, padding=1),
          layers.ConvBNReLU(64, 64, 3, padding=1),
          nn.Upsample(
              scale_factor=2, mode="bilinear", align_corners=False
          ),
      )
      # stage 0f
      self.decoder0_f = nn.Sequential(
          layers.ConvBNReLU(64, 64, 3, padding=1),
          layers.ConvBNReLU(64, 64, 3, padding=1),
          nn.Conv2D(64, 1 + 1 + 32, 3, padding=1),
      )
      self.init_weight()
  
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 HumanMatting object from inherited base classes: nn with example: obj = HumanMatting(backbone, pretrained, backbone_scale, refine_kernel_size, if_refine)