# Copyright 2021 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.
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# limitations under the License.

# Lint as: python3
"""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.common import registry_imports  # pylint: disable=unused-import
from official.core import exp_factory
from official.vision.beta.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)))
    else:
      processed_images = images
    expected_output = tf.image.resize(
        module.model(processed_images, training=False), [112, 112],
        method='bilinear')
    out = segmentation_fn(tf.constant(images))
    self.assertAllClose(out['predicted_masks'].numpy(), expected_output.numpy())


if __name__ == '__main__':
  tf.test.main()
