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#
# 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,
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"""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()