class_docstr: Feature Pyramid Network, see https://arxiv.org/abs/1612.03144
  
  Args:
      num_chan (int): number of feature channels
      min_level (int): lowest level of the backbone feature map to use
      max_level (int): highest level of the backbone feature map to use
      spatial_scale (list): feature map scaling factor
      has_extra_convs (bool): whether has extral convolutions in higher levels
      norm_type (str|None): normalization type, 'bn'/'sync_bn'/'affine_channel'
class_docstr_tok: ["Feature","Pyramid","Network",",","see","https",":","\/\/arxiv.org\/abs\/1612.03144","Args",":","num_chan","(","int",")",":","number","of","feature","channels","min_level","(","int",")",":","lowest","level","of","the","backbone","feature","map","to","use","max_level","(","int",")",":","highest","level","of","the","backbone","feature","map","to","use","spatial_scale","(","list",")",":","feature","map","scaling","factor","has_extra_convs","(","bool",")",":","whether","has","extral","convolutions","in","higher","levels","norm_type","(","str|None",")",":","normalization","type",",","'bn'\/'sync_bn'\/'affine_channel","'"]
code: def __init__(
      self,
      num_chan=256,
      min_level=2,
      max_level=6,
      spatial_scale=[1.0 / 32.0, 1.0 / 16.0, 1.0 / 8.0, 1.0 / 4.0],
      has_extra_convs=False,
      norm_type=None,
      freeze_norm=False,
  ):
      self.freeze_norm = freeze_norm
      self.num_chan = num_chan
      self.min_level = min_level
      self.max_level = max_level
      self.spatial_scale = spatial_scale
      self.has_extra_convs = has_extra_convs
      self.norm_type = norm_type
  
file_path: PaddleHub/modules/image/object_detection/faster_rcnn_resnet50_fpn_venus/fpn.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import copy
  from collections import OrderedDict
  from paddle import fluid
  from paddle.fluid.param_attr import ParamAttr
  from paddle.fluid.initializer import Xavier
  from paddle.fluid.regularizer import L2Decay
label_desc: Use image node_id 1 to create a new FPN object from inherited base classes: object with example: obj = FPN(num_chan, min_level, max_level, spatial_scale, has_extra_convs, norm_type, freeze_norm)