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())