text: # Copyright 2022 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.
  
  """Test for semantic segmentation export lib."""
  
  import io
  import os
  
  from absl.testing import parameterized
  import numpy as np
  from PIL import Image
  import tensorflow as tf
  
  from official.core import exp_factory
  from official.vision import registry_imports  # pylint: disable=unused-import
  from official.vision.serving import semantic_segmentation
  
  
  class SemanticSegmentationExportTest(tf.test.TestCase, parameterized.TestCase):
  
    def _get_segmentation_module(self, input_type):
      params = exp_factory.get_exp_config('mnv2_deeplabv3_pascal')
      segmentation_module = semantic_segmentation.SegmentationModule(
          params,
          batch_size=1,
          input_image_size=[112, 112],
          input_type=input_type)
      return segmentation_module
  
    def _export_from_module(self, module, input_type, save_directory):
      signatures = module.get_inference_signatures(
          {input_type: 'serving_default'})
      tf.saved_model.save(module, save_directory, signatures=signatures)
  
    def _get_dummy_input(self, input_type):
      """Get dummy input for the given input type."""
  
      if input_type == 'image_tensor':
        return tf.zeros((1, 112, 112, 3), dtype=np.uint8)
      elif input_type == 'image_bytes':
        image = Image.fromarray(np.zeros((112, 112, 3), dtype=np.uint8))
        byte_io = io.BytesIO()
        image.save(byte_io, 'PNG')
        return [byte_io.getvalue()]
      elif input_type == 'tf_example':
        image_tensor = tf.zeros((112, 112, 3), dtype=tf.uint8)
        encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).numpy()
        example = tf.train.Example(
            features=tf.train.Features(
                feature={
                    'image/encoded':
                        tf.train.Feature(
                            bytes_list=tf.train.BytesList(value=[encoded_jpeg])),
                })).SerializeToString()
        return [example]
      elif input_type == 'tflite':
        return tf.zeros((1, 112, 112, 3), dtype=np.float32)
  
    @parameterized.parameters(
        {'input_type': 'image_tensor'},
        {'input_type': 'image_bytes'},
        {'input_type': 'tf_example'},
        {'input_type': 'tflite'},
    )
    def test_export(self, input_type='image_tensor'):
      tmp_dir = self.get_temp_dir()
      module = self._get_segmentation_module(input_type)
  
      self._export_from_module(module, input_type, tmp_dir)
  
      self.assertTrue(os.path.exists(os.path.join(tmp_dir, 'saved_model.pb')))
      self.assertTrue(
          os.path.exists(os.path.join(tmp_dir, 'variables', 'variables.index')))
      self.assertTrue(
          os.path.exists(
              os.path.join(tmp_dir, 'variables',
                           'variables.data-00000-of-00001')))
  
      imported = tf.saved_model.load(tmp_dir)
      segmentation_fn = imported.signatures['serving_default']
  
      images = self._get_dummy_input(input_type)
      if input_type != 'tflite':
        processed_images, _ = tf.nest.map_structure(
            tf.stop_gradient,
            tf.map_fn(
                module._build_inputs,
                elems=tf.zeros((1, 112, 112, 3), dtype=tf.uint8),
                fn_output_signature=(tf.TensorSpec(
                    shape=[112, 112, 3], dtype=tf.float32),
                                     tf.TensorSpec(
                                         shape=[4, 2], dtype=tf.float32))))
      else:
        processed_images = images
      expected_output = tf.image.resize(
          module.model(processed_images, training=False)['logits'], [112, 112],
          method='bilinear')
      out = segmentation_fn(tf.constant(images))
      self.assertAllClose(out['logits'].numpy(), expected_output.numpy())
  
  
  if __name__ == '__main__':
    tf.test.main()
  