text: # Copyright 2020 The TensorFlow 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 absl.testing import parameterized
  import numpy as np
  import tensorflow as tf
  
  from official.vision.beta.projects.yolo.ops import preprocess_ops
  
  
  class PreprocessOpsTest(parameterized.TestCase, tf.test.TestCase):
  
    @parameterized.parameters((416, 416, 5, 300, 300), (100, 200, 6, 50, 50))
    def test_resize_crop_filter(self, default_width, default_height, num_boxes,
                                target_width, target_height):
      image = tf.convert_to_tensor(
          np.random.rand(default_width, default_height, 3))
      boxes = tf.convert_to_tensor(np.random.rand(num_boxes, 4))
      resized_image, resized_boxes = preprocess_ops.resize_crop_filter(
          image, boxes, default_width, default_height, target_width,
          target_height)
      resized_image_shape = tf.shape(resized_image)
      resized_boxes_shape = tf.shape(resized_boxes)
      self.assertAllEqual([default_height, default_width, 3],
                          resized_image_shape.numpy())
      self.assertAllEqual([num_boxes, 4], resized_boxes_shape.numpy())
  
    @parameterized.parameters((7, 7., 5.), (25, 35., 45.))
    def test_translate_boxes(self, num_boxes, translate_x, translate_y):
      boxes = tf.convert_to_tensor(np.random.rand(num_boxes, 4))
      translated_boxes = preprocess_ops.translate_boxes(
          boxes, translate_x, translate_y)
      translated_boxes_shape = tf.shape(translated_boxes)
      self.assertAllEqual([num_boxes, 4], translated_boxes_shape.numpy())
  
    @parameterized.parameters((100, 200, 75., 25.), (400, 600, 25., 75.))
    def test_translate_image(self, image_height, image_width, translate_x,
                             translate_y):
      image = tf.convert_to_tensor(np.random.rand(image_height, image_width, 4))
      translated_image = preprocess_ops.translate_image(
          image, translate_x, translate_y)
      translated_image_shape = tf.shape(translated_image)
      self.assertAllEqual([image_height, image_width, 4],
                          translated_image_shape.numpy())
  
    @parameterized.parameters(([1, 2], 20, 0), ([13, 2, 4], 15, 0))
    def test_pad_max_instances(self, input_shape, instances, pad_axis):
      expected_output_shape = input_shape
      expected_output_shape[pad_axis] = instances
      output = preprocess_ops.pad_max_instances(
          np.ones(input_shape), instances, pad_axis=pad_axis)
      self.assertAllEqual(expected_output_shape, tf.shape(output).numpy())
  
  
  if __name__ == '__main__':
    tf.test.main()
  