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
  import keras_retinanet.layers
  
  import math
  import numpy as np
  
  
  class TestAnchors(object):
      def test_simple(self):
          # create simple Anchors layer
          anchors_layer = keras_retinanet.layers.Anchors(
              size=32,
              stride=8,
              ratios=np.array([1], keras.backend.floatx()),
              scales=np.array([1], keras.backend.floatx()),
          )
  
          # create fake features input (only shape is used anyway)
          features = np.zeros((1, 2, 2, 1024), dtype=keras.backend.floatx())
          features = keras.backend.variable(features)
  
          # call the Anchors layer
          anchors = anchors_layer.call(features)
          anchors = keras.backend.eval(anchors)
  
          # expected anchor values
          expected = np.array([[
              [-12, -12, 20, 20],
              [-4 , -12, 28, 20],
              [-12, -4 , 20, 28],
              [-4 , -4 , 28, 28],
          ]], dtype=keras.backend.floatx())
  
          # test anchor values
          np.testing.assert_array_equal(anchors, expected)
  
      # mark test to fail
      def test_mini_batch(self):
          # create simple Anchors layer
          anchors_layer = keras_retinanet.layers.Anchors(
              size=32,
              stride=8,
              ratios=np.array([1], dtype=keras.backend.floatx()),
              scales=np.array([1], dtype=keras.backend.floatx()),
          )
  
          # create fake features input with batch_size=2
          features = np.zeros((2, 2, 2, 1024), dtype=keras.backend.floatx())
          features = keras.backend.variable(features)
  
          # call the Anchors layer
          anchors = anchors_layer.call(features)
          anchors = keras.backend.eval(anchors)
  
          # expected anchor values
          expected = np.array([[
              [-12, -12, 20, 20],
              [-4 , -12, 28, 20],
              [-12, -4 , 20, 28],
              [-4 , -4 , 28, 28],
          ]], dtype=keras.backend.floatx())
          expected = np.tile(expected, (2, 1, 1))
  
          # test anchor values
          np.testing.assert_array_equal(anchors, expected)
  
  
  class TestUpsampleLike(object):
      def test_simple(self):
          # create simple UpsampleLike layer
          upsample_like_layer = keras_retinanet.layers.UpsampleLike()
  
          # create input source
          source   = np.zeros((1, 2, 2, 1), dtype=keras.backend.floatx())
          source   = keras.backend.variable(source)
          target   = np.zeros((1, 5, 5, 1), dtype=keras.backend.floatx())
          expected = target
          target   = keras.backend.variable(target)
  
          # compute output
          actual = upsample_like_layer.call([source, target])
          actual = keras.backend.eval(actual)
  
          np.testing.assert_array_equal(actual, expected)
  
      def test_mini_batch(self):
          # create simple UpsampleLike layer
          upsample_like_layer = keras_retinanet.layers.UpsampleLike()
  
          # create input source
          source = np.zeros((2, 2, 2, 1), dtype=keras.backend.floatx())
          source = keras.backend.variable(source)
  
          target   = np.zeros((2, 5, 5, 1), dtype=keras.backend.floatx())
          expected = target
          target   = keras.backend.variable(target)
  
          # compute output
          actual = upsample_like_layer.call([source, target])
          actual = keras.backend.eval(actual)
  
          np.testing.assert_array_equal(actual, expected)
  
  
  class TestRegressBoxes(object):
      def test_simple(self):
          mean = [0, 0, 0, 0]
          std  = [0.1, 0.1, 0.2, 0.2]
  
          # create simple RegressBoxes layer
          regress_boxes_layer = keras_retinanet.layers.RegressBoxes(mean=mean, std=std)
  
          # create input
          anchors = np.array([[
              [0 , 0 , 10 , 10 ],
              [50, 50, 100, 100],
              [20, 20, 40 , 40 ],
          ]], dtype=keras.backend.floatx())
          anchors = keras.backend.variable(anchors)
  
          regression = np.array([[
              [0  , 0  , 0  , 0  ],
              [0.1, 0.1, 0  , 0  ],
              [0  , 0  , 0.1, 0.1],
          ]], dtype=keras.backend.floatx())
          regression = keras.backend.variable(regression)
  
          # compute output
          actual = regress_boxes_layer.call([anchors, regression])
          actual = keras.backend.eval(actual)
  
          # compute expected output
          expected = np.array([[
              [0 , 0 , 10 , 10 ],
              [50 + (0.1 * std[0] + mean[0]) * 50, 50 + (0.1 * std[1] + mean[1]) * 50, 100 + (0.1 * std[0] + mean[0]) * 50, 100 + (0.1 * std[1] + mean[1]) * 50],
              [30 - math.e ** (0.1 * std[2] + mean[2]) * 20 * 0.5, 30 - math.e ** (0.1 * std[3] + mean[3]) * 20 * 0.5, 30 + math.e ** (0.1 * std[2] + mean[2]) * 20 * 0.5, 30 + math.e ** (0.1 * std[3] + mean[3]) * 20 * 0.5],
          ]], dtype=keras.backend.floatx())
  
          np.testing.assert_array_almost_equal(actual, expected, decimal=2)
  
      # mark test to fail
      def test_mini_batch(self):
          mean = [0, 0, 0, 0]
          std  = [0.1, 0.1, 0.2, 0.2]
  
          # create simple RegressBoxes layer
          regress_boxes_layer = keras_retinanet.layers.RegressBoxes(mean=mean, std=std)
  
          # create input
          anchors = np.array([
              [
                  [0 , 0 , 10 , 10 ],  # 1
                  [50, 50, 100, 100],  # 2
                  [20, 20, 40 , 40 ],  # 3
              ],
              [
                  [20, 20, 40 , 40 ],  # 3
                  [0 , 0 , 10 , 10 ],  # 1
                  [50, 50, 100, 100],  # 2
              ],
          ], dtype=keras.backend.floatx())
          anchors = keras.backend.variable(anchors)
  
          regression = np.array([
              [
                  [0  , 0  , 0  , 0  ],  # 1
                  [0.1, 0.1, 0  , 0  ],  # 2
                  [0  , 0  , 0.1, 0.1],  # 3
              ],
              [
                  [0  , 0  , 0.1, 0.1],  # 3
                  [0  , 0  , 0  , 0  ],  # 1
                  [0.1, 0.1, 0  , 0  ],  # 2
              ],
          ], dtype=keras.backend.floatx())
          regression = keras.backend.variable(regression)
  
          # compute output
          actual = regress_boxes_layer.call([anchors, regression])
          actual = keras.backend.eval(actual)
  
          # compute expected output
          expected = np.array([
              [
                  [0 , 0 , 10 , 10 ],  # 1
                  [50.5, 50.5, 100.5, 100.5],  # 2
                  [30 - math.e ** (0.1 * std[2] + mean[2]) * 20 * 0.5, 30 - math.e ** (0.1 * std[3] + mean[3]) * 20 * 0.5, 30 + math.e ** (0.1 * std[2] + mean[2]) * 20 * 0.5, 30 + math.e ** (0.1 * std[3] + mean[3]) * 20 * 0.5],  # 3
              ],
              [
                  [30 - math.e ** (0.1 * std[2] + mean[2]) * 20 * 0.5, 30 - math.e ** (0.1 * std[3] + mean[3]) * 20 * 0.5, 30 + math.e ** (0.1 * std[2] + mean[2]) * 20 * 0.5, 30 + math.e ** (0.1 * std[3] + mean[3]) * 20 * 0.5],  # 3
                  [0 , 0 , 10 , 10 ],  # 1
                  [50.5, 50.5, 100.5, 100.5],  # 2
              ],
          ], dtype=keras.backend.floatx())
  
          np.testing.assert_array_almost_equal(actual, expected, decimal=2)
  