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())
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_network_creation(self, input_size, model_id):
"""Test creation of UNet3D family models."""
tf.keras.backend.set_image_data_format('channels_last')
# `input_size` consists of [spatial size, volume size].
inputs = tf.keras.Input(
shape=(input_size[0], input_size[0], input_size[1], 3), batch_size=1)
backbone = unet_3d.UNet3D(model_id=model_id)
network = unet_3d_decoder.UNet3DDecoder(
model_id=model_id, input_specs=backbone.output_specs)
endpoints = backbone(inputs)
feats = network(endpoints)
self.assertIn('1', feats)
self.assertAllEqual([1, input_size[0], input_size[0], input_size[1], 64],
feats['1'].shape.as_list())