class_docstr: The FPN Neck implementation in paddle.
  
  Args:
      fpn_inplanes (list, optional): Input channels list(the feature channels from backbone) for lateral_conv constraction. Default: [256, 512, 1024, 2048].
      fpn_outplanes (int, optional): The output channels. Default: 256.
class_docstr_tok: ["The","FPN","Neck","implementation","in","paddle",".","Args",":","fpn_inplanes","(","list",",","optional",")",":","Input","channels","list","(","the","feature","channels","from","backbone",")","for","lateral_conv","constraction",".","Default",":","[","256",",","512",",","1024",",","2048","]",".","fpn_outplanes","(","int",",","optional",")",":","The","output","channels",".","Default",":","256","."]
code: def __init__(
      self,
      fpn_inplanes=[256, 512, 1024, 2048],
      fpn_outplanes=256,
  ):
      super(FPNNeck, self).__init__()
      self.lateral_convs = []
      self.fpn_out = []
  
      # FPN head
      for fpn_inplane in fpn_inplanes:
          self.lateral_convs.append(
              nn.Sequential(
                  nn.Conv2D(fpn_inplane, fpn_outplanes, 1),
                  layers.SyncBatchNorm(fpn_outplanes),
                  nn.ReLU(),
              )
          )
          self.fpn_out.append(
              nn.Sequential(
                  layers.ConvBNReLU(
                      fpn_outplanes, fpn_outplanes, 3, bias_attr=False
                  )
              )
          )
  
      self.lateral_convs = nn.LayerList(self.lateral_convs)
      self.fpn_out = nn.LayerList(self.fpn_out)
  
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 1 to create a new FPNNeck object from inherited base classes: nn with example: obj = FPNNeck(fpn_inplanes, fpn_outplanes)