text: # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
  from collections import OrderedDict
  
  from torch import nn
  
  from maskrcnn_benchmark.modeling import registry
  from maskrcnn_benchmark.modeling.make_layers import conv_with_kaiming_uniform
  from . import fpn as fpn_module
  from . import resnet
  
  
  @registry.BACKBONES.register("R-50-C4")
  @registry.BACKBONES.register("R-50-C5")
  @registry.BACKBONES.register("R-101-C4")
  @registry.BACKBONES.register("R-101-C5")
  def build_resnet_backbone(cfg):
      body = resnet.ResNet(cfg)
      model = nn.Sequential(OrderedDict([("body", body)]))
      return model
  
  
  @registry.BACKBONES.register("R-50-FPN")
  @registry.BACKBONES.register("R-101-FPN")
  @registry.BACKBONES.register("R-152-FPN")
  def build_resnet_fpn_backbone(cfg):
      body = resnet.ResNet(cfg)
      in_channels_stage2 = cfg.MODEL.RESNETS.RES2_OUT_CHANNELS
      out_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS
      fpn = fpn_module.FPN(
          in_channels_list=[
              in_channels_stage2,
              in_channels_stage2 * 2,
              in_channels_stage2 * 4,
              in_channels_stage2 * 8,
          ],
          out_channels=out_channels,
          conv_block=conv_with_kaiming_uniform(
              cfg.MODEL.FPN.USE_GN, cfg.MODEL.FPN.USE_RELU
          ),
          top_blocks=fpn_module.LastLevelMaxPool(),
      )
      model = nn.Sequential(OrderedDict([("body", body), ("fpn", fpn)]))
      return model
  
  
  def build_backbone(cfg):
      assert cfg.MODEL.BACKBONE.CONV_BODY in registry.BACKBONES, \
          "cfg.MODEL.BACKBONE.CONV_BODY: {} are not registered in registry".format(
              cfg.MODEL.BACKBONE.CONV_BODY
          )
      return registry.BACKBONES[cfg.MODEL.BACKBONE.CONV_BODY](cfg)
  