official/projects/volumetric_models/modeling/backbones/unet_3d_test.py
# Copyright 2024 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.
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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"""Tests for 3D UNet backbone."""
# Import libraries
from absl.testing import parameterized
import tensorflow as tf, tf_keras
from official.projects.volumetric_models.modeling.backbones import unet_3d
class UNet3DTest(parameterized.TestCase, tf.test.TestCase):
@parameterized.parameters(
([128, 64], 4),
([256, 128], 6),
)
def test_network_creation(self, input_size, model_id):
"""Test creation of UNet3D family models."""
tf_keras.backend.set_image_data_format('channels_last')
network = unet_3d.UNet3D(model_id=model_id)
inputs = tf_keras.Input(
shape=(input_size[0], input_size[0], input_size[1], 3), batch_size=1)
endpoints = network(inputs)
for layer_depth in range(model_id):
self.assertAllEqual([
1, input_size[0] / 2**layer_depth, input_size[0] / 2**layer_depth,
input_size[1] / 2**layer_depth, 64 * 2**layer_depth
], endpoints[str(layer_depth + 1)].shape.as_list())
def test_serialize_deserialize(self):
# Create a network object that sets all of its config options.
kwargs = dict(
model_id=4,
pool_size=(2, 2, 2),
kernel_size=(3, 3, 3),
activation='relu',
base_filters=32,
kernel_regularizer=None,
norm_momentum=0.99,
norm_epsilon=0.001,
use_sync_bn=False,
use_batch_normalization=True)
network = unet_3d.UNet3D(**kwargs)
expected_config = dict(kwargs)
self.assertEqual(network.get_config(), expected_config)
# Create another network object from the first object's config.
new_network = unet_3d.UNet3D.from_config(network.get_config())
# Validate that the config can be forced to JSON.
_ = new_network.to_json()
# If the serialization was successful, the new config should match the old.
self.assertAllEqual(network.get_config(), new_network.get_config())
if __name__ == '__main__':
tf.test.main()