text: """
  Copyright 2018 vidosits (https://github.com/vidosits/)
  
  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 warnings
  import pytest
  import numpy as np
  from tensorflow import keras
  from keras_retinanet import losses
  from keras_retinanet.models.densenet import DenseNetBackbone
  
  parameters = ['densenet121']
  
  
  @pytest.mark.parametrize("backbone", parameters)
  def test_backbone(backbone):
      # ignore warnings in this test
      warnings.simplefilter('ignore')
  
      num_classes = 10
  
      inputs = np.zeros((1, 200, 400, 3), dtype=np.float32)
      targets = [np.zeros((1, 14814, 5), dtype=np.float32), np.zeros((1, 14814, num_classes + 1))]
  
      inp = keras.layers.Input(inputs[0].shape)
  
      densenet_backbone = DenseNetBackbone(backbone)
      model = densenet_backbone.retinanet(num_classes=num_classes, inputs=inp)
      model.summary()
  
      # compile model
      model.compile(
          loss={
              'regression': losses.smooth_l1(),
              'classification': losses.focal()
          },
          optimizer=keras.optimizers.Adam(lr=1e-5, clipnorm=0.001))
  
      model.fit(inputs, targets, batch_size=1)
  