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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.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
#
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"""Test for panoptic image 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.beta.projects.panoptic_maskrcnn.configs import panoptic_maskrcnn as cfg  # pylint: disable=unused-import
from official.vision.beta.projects.panoptic_maskrcnn.modeling import factory
from official.vision.beta.projects.panoptic_maskrcnn.serving import panoptic_segmentation
from official.vision.beta.projects.panoptic_maskrcnn.tasks import panoptic_maskrcnn as task  # pylint: disable=unused-import


class PanopticSegmentationExportTest(tf.test.TestCase, parameterized.TestCase):

  def _get_panoptic_segmentation_module(self, experiment_name):
    params = exp_factory.get_exp_config(experiment_name)
    params.task.model.backbone.resnet.model_id = 18
    params.task.model.detection_generator.nms_version = 'batched'
    input_specs = tf.keras.layers.InputSpec(shape=[1, 128, 128, 3])
    model = factory.build_panoptic_maskrcnn(
        input_specs=input_specs, model_config=params.task.model)
    panoptic_segmentation_module = panoptic_segmentation.PanopticSegmentationModule(
        params, model=model, batch_size=1, input_image_size=[128, 128])
    return panoptic_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, batch_size, image_size):
    """Get dummy input for the given input type."""
    h, w = image_size

    if input_type == 'image_tensor':
      return tf.zeros((batch_size, h, w, 3), dtype=np.uint8)
    elif input_type == 'image_bytes':
      image = Image.fromarray(np.zeros((h, w, 3), dtype=np.uint8))
      byte_io = io.BytesIO()
      image.save(byte_io, 'PNG')
      return [byte_io.getvalue() for b in range(batch_size)]
    elif input_type == 'tf_example':
      image_tensor = tf.zeros((h, w, 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 for b in range(batch_size)]

  @parameterized.parameters(
      ('image_tensor', 'panoptic_fpn_coco'),
      ('image_bytes', 'panoptic_fpn_coco'),
      ('tf_example', 'panoptic_fpn_coco'),
  )
  def test_export(self, input_type, experiment_name):
    tmp_dir = self.get_temp_dir()
    module = self._get_panoptic_segmentation_module(experiment_name)

    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)
    detection_fn = imported.signatures['serving_default']

    images = self._get_dummy_input(
        input_type, batch_size=1, image_size=[128, 128])

    processed_images, anchor_boxes, image_info = module._build_inputs(
        tf.zeros((128, 128, 3), dtype=tf.uint8))
    image_info = tf.expand_dims(image_info, 0)
    processed_images = tf.expand_dims(processed_images, 0)
    for l, l_boxes in anchor_boxes.items():
      anchor_boxes[l] = tf.expand_dims(l_boxes, 0)

    expected_outputs = module.model(
        images=processed_images,
        image_info=image_info,
        anchor_boxes=anchor_boxes,
        training=False)
    outputs = detection_fn(tf.constant(images))

    self.assertAllClose(outputs['num_detections'].numpy(),
                        expected_outputs['num_detections'].numpy())

  def test_build_model_fail_with_none_batch_size(self):
    params = exp_factory.get_exp_config('panoptic_fpn_coco')
    input_specs = tf.keras.layers.InputSpec(shape=[1, 128, 128, 3])
    model = factory.build_panoptic_maskrcnn(
        input_specs=input_specs, model_config=params.task.model)
    with self.assertRaisesRegex(
        ValueError,
        'batch_size cannot be None for panoptic segmentation model.'):
      _ = panoptic_segmentation.PanopticSegmentationModule(
          params, model=model, batch_size=None, input_image_size=[128, 128])

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