text: # 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.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  # Lint as: python3
  """Tests for segmentation network."""
  
  from absl.testing import parameterized
  import numpy as np
  import tensorflow as tf
  from official.projects.volumetric_models.modeling import backbones
  from official.projects.volumetric_models.modeling import decoders
  from official.projects.volumetric_models.modeling.heads import segmentation_heads_3d
  from official.vision.beta.modeling import segmentation_model
  
  
  class SegmentationNetworkUNet3DTest(parameterized.TestCase, tf.test.TestCase):
  
    @parameterized.parameters(
        ([32, 32], 4),
        ([64, 64], 4),
        ([64, 64], 2),
        ([128, 64], 2),
    )
    def test_segmentation_network_unet3d_creation(self, input_size, depth):
      """Test for creation of a segmentation network."""
      num_classes = 2
      inputs = np.random.rand(2, input_size[0], input_size[0], input_size[1], 3)
      tf.keras.backend.set_image_data_format('channels_last')
      backbone = backbones.UNet3D(model_id=depth)
  
      decoder = decoders.UNet3DDecoder(
          model_id=depth, input_specs=backbone.output_specs)
      head = segmentation_heads_3d.SegmentationHead3D(
          num_classes, level=1, num_convs=0)
  
      model = segmentation_model.SegmentationModel(
          backbone=backbone, decoder=decoder, head=head)
  
      outputs = model(inputs)
      self.assertAllEqual(
          [2, input_size[0], input_size[0], input_size[1], num_classes],
          outputs['logits'].numpy().shape)
  
    def test_serialize_deserialize(self):
      """Validate the network can be serialized and deserialized."""
      num_classes = 3
      backbone = backbones.UNet3D(model_id=4)
      decoder = decoders.UNet3DDecoder(
          model_id=4, input_specs=backbone.output_specs)
      head = segmentation_heads_3d.SegmentationHead3D(
          num_classes, level=1, num_convs=0)
      model = segmentation_model.SegmentationModel(
          backbone=backbone, decoder=decoder, head=head)
  
      config = model.get_config()
      new_model = segmentation_model.SegmentationModel.from_config(config)
  
      # Validate that the config can be forced to JSON.
      _ = new_model.to_json()
  
      # If the serialization was successful, the new config should match the old.
      self.assertAllEqual(model.get_config(), new_model.get_config())
  
  
  if __name__ == '__main__':
    tf.test.main()
  