text: """
  Copyright 2017-2018 Fizyr (https://fizyr.com)
  
  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.
  """
  
  import keras
  from keras.utils import get_file
  import keras_resnet
  import keras_resnet.models
  
  from . import retinanet
  from . import Backbone
  from ..utils.image import preprocess_image
  
  
  class ResNetBackbone(Backbone):
      """ Describes backbone information and provides utility functions.
      """
  
      def __init__(self, backbone):
          super(ResNetBackbone, self).__init__(backbone)
          self.custom_objects.update(keras_resnet.custom_objects)
  
      def retinanet(self, *args, **kwargs):
          """ Returns a retinanet model using the correct backbone.
          """
          return resnet_retinanet(*args, backbone=self.backbone, **kwargs)
  
      def download_imagenet(self):
          """ Downloads ImageNet weights and returns path to weights file.
          """
          resnet_filename = 'ResNet-{}-model.keras.h5'
          resnet_resource = 'https://github.com/fizyr/keras-models/releases/download/v0.0.1/{}'.format(resnet_filename)
          depth = int(self.backbone.replace('resnet', ''))
  
          filename = resnet_filename.format(depth)
          resource = resnet_resource.format(depth)
          if depth == 50:
              checksum = '3e9f4e4f77bbe2c9bec13b53ee1c2319'
          elif depth == 101:
              checksum = '05dc86924389e5b401a9ea0348a3213c'
          elif depth == 152:
              checksum = '6ee11ef2b135592f8031058820bb9e71'
  
          return get_file(
              filename,
              resource,
              cache_subdir='models',
              md5_hash=checksum
          )
  
      def validate(self):
          """ Checks whether the backbone string is correct.
          """
          allowed_backbones = ['resnet50', 'resnet101', 'resnet152']
          backbone = self.backbone.split('_')[0]
  
          if backbone not in allowed_backbones:
              raise ValueError('Backbone (\'{}\') not in allowed backbones ({}).'.format(backbone, allowed_backbones))
  
      def preprocess_image(self, inputs):
          """ Takes as input an image and prepares it for being passed through the network.
          """
          return preprocess_image(inputs, mode='caffe')
  
  
  def resnet_retinanet(num_classes, backbone='resnet50', inputs=None, modifier=None, **kwargs):
      """ Constructs a retinanet model using a resnet backbone.
  
      Args
          num_classes: Number of classes to predict.
          backbone: Which backbone to use (one of ('resnet50', 'resnet101', 'resnet152')).
          inputs: The inputs to the network (defaults to a Tensor of shape (None, None, 3)).
          modifier: A function handler which can modify the backbone before using it in retinanet (this can be used to freeze backbone layers for example).
  
      Returns
          RetinaNet model with a ResNet backbone.
      """
      # choose default input
      if inputs is None:
          if keras.backend.image_data_format() == 'channels_first':
              inputs = keras.layers.Input(shape=(9, None, None))
          else:
              inputs = keras.layers.Input(shape=(None, None, 9))
  
      # create the resnet backbone
      if backbone == 'resnet50':
          resnet = keras_resnet.models.ResNet50(inputs, include_top=False, freeze_bn=True)
      elif backbone == 'resnet101':
          resnet = keras_resnet.models.ResNet101(inputs, include_top=False, freeze_bn=True)
      elif backbone == 'resnet152':
          resnet = keras_resnet.models.ResNet152(inputs, include_top=False, freeze_bn=True)
      else:
          raise ValueError('Backbone (\'{}\') is invalid.'.format(backbone))
  
      # invoke modifier if given
      if modifier:
          resnet = modifier(resnet)
  
      # create the full model
      return retinanet.retinanet(inputs=inputs, num_classes=num_classes, backbone_layers=resnet.outputs[1:], **kwargs)
  
  
  def resnet50_retinanet(num_classes, inputs=None, **kwargs):
      return resnet_retinanet(num_classes=num_classes, backbone='resnet50', inputs=inputs, **kwargs)
  
  
  def resnet101_retinanet(num_classes, inputs=None, **kwargs):
      return resnet_retinanet(num_classes=num_classes, backbone='resnet101', inputs=inputs, **kwargs)
  
  
  def resnet152_retinanet(num_classes, inputs=None, **kwargs):
      return resnet_retinanet(num_classes=num_classes, backbone='resnet152', inputs=inputs, **kwargs)
  