text: # Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from collections import OrderedDict
  import copy
  from paddle import fluid
  from paddle.fluid.param_attr import ParamAttr
  from paddle.fluid.initializer import Xavier
  from paddle.fluid.regularizer import L2Decay
  
  from ppdet.core.workspace import register
  from ppdet.modeling.ops import ConvNorm
  
  __all__ = ['FPN']
  
  
  @register
  class FPN(object):
      """
      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'
      """
      __shared__ = ['norm_type', 'freeze_norm']
  
      def __init__(self,
                   num_chan=256,
                   min_level=2,
                   max_level=6,
                   spatial_scale=[1. / 32., 1. / 16., 1. / 8., 1. / 4.],
                   has_extra_convs=False,
                   norm_type=None,
                   freeze_norm=False,
                   use_c5=True):
          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
          self.use_c5 = use_c5
  
      def _add_topdown_lateral(self, body_name, body_input, upper_output):
          lateral_name = 'fpn_inner_' + body_name + '_lateral'
          topdown_name = 'fpn_topdown_' + body_name
          fan = body_input.shape[1]
          if self.norm_type:
              initializer = Xavier(fan_out=fan)
              lateral = ConvNorm(
                  body_input,
                  self.num_chan,
                  1,
                  initializer=initializer,
                  norm_type=self.norm_type,
                  freeze_norm=self.freeze_norm,
                  name=lateral_name,
                  norm_name=lateral_name)
          else:
              lateral = fluid.layers.conv2d(
                  body_input,
                  self.num_chan,
                  1,
                  param_attr=ParamAttr(
                      name=lateral_name + "_w", initializer=Xavier(fan_out=fan)),
                  bias_attr=ParamAttr(
                      name=lateral_name + "_b",
                      learning_rate=2.,
                      regularizer=L2Decay(0.)),
                  name=lateral_name)
          topdown = fluid.layers.resize_nearest(
              upper_output, scale=2., name=topdown_name)
  
          return lateral + topdown
  
      def get_output(self, body_dict):
          """
          Add FPN onto backbone.
  
          Args:
              body_dict(OrderedDict): Dictionary of variables and each element is the
                  output of backbone.
  
          Return:
              fpn_dict(OrderedDict): A dictionary represents the output of FPN with
                  their name.
              spatial_scale(list): A list of multiplicative spatial scale factor.
          """
          spatial_scale = copy.deepcopy(self.spatial_scale)
          body_name_list = list(body_dict.keys())[::-1]
          num_backbone_stages = len(body_name_list)
          self.fpn_inner_output = [[] for _ in range(num_backbone_stages)]
          fpn_inner_name = 'fpn_inner_' + body_name_list[0]
          body_input = body_dict[body_name_list[0]]
          fan = body_input.shape[1]
          if self.norm_type:
              initializer = Xavier(fan_out=fan)
              self.fpn_inner_output[0] = ConvNorm(
                  body_input,
                  self.num_chan,
                  1,
                  initializer=initializer,
                  norm_type=self.norm_type,
                  freeze_norm=self.freeze_norm,
                  name=fpn_inner_name,
                  norm_name=fpn_inner_name)
          else:
              self.fpn_inner_output[0] = fluid.layers.conv2d(
                  body_input,
                  self.num_chan,
                  1,
                  param_attr=ParamAttr(
                      name=fpn_inner_name + "_w",
                      initializer=Xavier(fan_out=fan)),
                  bias_attr=ParamAttr(
                      name=fpn_inner_name + "_b",
                      learning_rate=2.,
                      regularizer=L2Decay(0.)),
                  name=fpn_inner_name)
          for i in range(1, num_backbone_stages):
              body_name = body_name_list[i]
              body_input = body_dict[body_name]
              top_output = self.fpn_inner_output[i - 1]
              fpn_inner_single = self._add_topdown_lateral(body_name, body_input,
                                                           top_output)
              self.fpn_inner_output[i] = fpn_inner_single
          fpn_dict = {}
          fpn_name_list = []
          for i in range(num_backbone_stages):
              fpn_name = 'fpn_' + body_name_list[i]
              fan = self.fpn_inner_output[i].shape[1] * 3 * 3
              if self.norm_type:
                  initializer = Xavier(fan_out=fan)
                  fpn_output = ConvNorm(
                      self.fpn_inner_output[i],
                      self.num_chan,
                      3,
                      initializer=initializer,
                      norm_type=self.norm_type,
                      freeze_norm=self.freeze_norm,
                      name=fpn_name,
                      norm_name=fpn_name)
              else:
                  fpn_output = fluid.layers.conv2d(
                      self.fpn_inner_output[i],
                      self.num_chan,
                      filter_size=3,
                      padding=1,
                      param_attr=ParamAttr(
                          name=fpn_name + "_w", initializer=Xavier(fan_out=fan)),
                      bias_attr=ParamAttr(
                          name=fpn_name + "_b",
                          learning_rate=2.,
                          regularizer=L2Decay(0.)),
                      name=fpn_name)
              fpn_dict[fpn_name] = fpn_output
              fpn_name_list.append(fpn_name)
          if not self.has_extra_convs and self.max_level - self.min_level == len(
                  spatial_scale):
              body_top_name = fpn_name_list[0]
              body_top_extension = fluid.layers.pool2d(
                  fpn_dict[body_top_name],
                  1,
                  'max',
                  pool_stride=2,
                  name=body_top_name + '_subsampled_2x')
              fpn_dict[body_top_name + '_subsampled_2x'] = body_top_extension
              fpn_name_list.insert(0, body_top_name + '_subsampled_2x')
              spatial_scale.insert(0, spatial_scale[0] * 0.5)
          # Coarser FPN levels introduced for RetinaNet
          highest_backbone_level = self.min_level + len(spatial_scale) - 1
          if self.has_extra_convs and self.max_level > highest_backbone_level:
              if self.use_c5:
                  fpn_blob = body_dict[body_name_list[0]]
              else:
                  fpn_blob = fpn_dict[fpn_name_list[0]]
              for i in range(highest_backbone_level + 1, self.max_level + 1):
                  fpn_blob_in = fpn_blob
                  fpn_name = 'fpn_' + str(i)
                  if i > highest_backbone_level + 1:
                      fpn_blob_in = fluid.layers.relu(fpn_blob)
                  fan = fpn_blob_in.shape[1] * 3 * 3
                  fpn_blob = fluid.layers.conv2d(
                      input=fpn_blob_in,
                      num_filters=self.num_chan,
                      filter_size=3,
                      stride=2,
                      padding=1,
                      param_attr=ParamAttr(
                          name=fpn_name + "_w", initializer=Xavier(fan_out=fan)),
                      bias_attr=ParamAttr(
                          name=fpn_name + "_b",
                          learning_rate=2.,
                          regularizer=L2Decay(0.)),
                      name=fpn_name)
                  fpn_dict[fpn_name] = fpn_blob
                  fpn_name_list.insert(0, fpn_name)
                  spatial_scale.insert(0, spatial_scale[0] * 0.5)
          res_dict = OrderedDict([(k, fpn_dict[k]) for k in fpn_name_list])
          return res_dict, spatial_scale
  