class_docstr: RCNN bbox head with Two fc layers to extract feature
  
  Args:
      in_channel (int): Input channel which can be derived by from_config
      out_channel (int): Output channel
      resolution (int): Resolution of input feature map, default 7
class_docstr_tok: ["RCNN","bbox","head","with","Two","fc","layers","to","extract","feature","Args",":","in_channel","(","int",")",":","Input","channel","which","can","be","derived","by","from_config","out_channel","(","int",")",":","Output","channel","resolution","(","int",")",":","Resolution","of","input","feature","map",",","default","7"]
code: def __init__(self, in_channel=256, out_channel=1024, resolution=7):
      super(TwoFCHead, self).__init__()
      self.in_channel = in_channel
      self.out_channel = out_channel
      fan = in_channel * resolution * resolution
      self.fc6 = nn.Linear(
          in_channel * resolution * resolution,
          out_channel,
          weight_attr=paddle.ParamAttr(
              initializer=XavierUniform(fan_out=fan)
          ),
      )
      self.fc6.skip_quant = True
  
      self.fc7 = nn.Linear(
          out_channel,
          out_channel,
          weight_attr=paddle.ParamAttr(initializer=XavierUniform()),
      )
      self.fc7.skip_quant = True
  
imports: import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn.initializer import Normal, XavierUniform, KaimingNormal
  from paddle.regularizer import L2Decay
  from ppdet.core.workspace import register, create
  from .roi_extractor import RoIAlign
  from ..shape_spec import ShapeSpec
  from ..bbox_utils import bbox2delta
  from ..cls_utils import _get_class_default_kwargs
  from ppdet.modeling.layers import ConvNormLayer
label_desc: Use image node_id 1 to create a new TwoFCHead object from inherited base classes: nn with example: obj = TwoFCHead(in_channel, out_channel, resolution)