def test_serialize_deserialize(self): # Create a network object that sets all of its config options. kwargs = dict( model_id='darknet53', min_level=3, max_level=5, use_sync_bn=False, activation='relu', norm_momentum=0.99, norm_epsilon=0.001, kernel_initializer='VarianceScaling', kernel_regularizer=None, bias_regularizer=None, ) network = darknet.Darknet(**kwargs) expected_config = dict(kwargs) self.assertEqual(network.get_config(), expected_config) # Create another network object from the first object's config. new_network = darknet.Darknet.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, endpoint_filter_scale, scale_final, dilate): """Test creation of ResNet family models.""" tf.keras.backend.set_image_data_format('channels_last') network = darknet.Darknet( model_id=model_id, min_level=3, max_level=5, dilate=dilate) self.assertEqual(network.model_id, model_id) inputs = tf.keras.Input(shape=(input_size, input_size, 3), batch_size=1) endpoints = network(inputs) if not dilate: self.assertAllEqual([ 1, input_size / 2**3, input_size / 2**3, 128 * endpoint_filter_scale ], endpoints['3'].shape.as_list()) self.assertAllEqual([ 1, input_size / 2**4, input_size / 2**4, 256 * endpoint_filter_scale ], endpoints['4'].shape.as_list()) self.assertAllEqual([ 1, input_size / 2**5, input_size / 2**5, 512 * endpoint_filter_scale * scale_final ], endpoints['5'].shape.as_list()) else: self.assertAllEqual([ 1, input_size / 2**3, input_size / 2**3, 128 * endpoint_filter_scale ], endpoints['3'].shape.as_list()) self.assertAllEqual([ 1, input_size / 2**3, input_size / 2**3, 256 * endpoint_filter_scale ], endpoints['4'].shape.as_list()) self.assertAllEqual([ 1, input_size / 2**3, input_size / 2**3, 512 * endpoint_filter_scale * scale_final ], endpoints['5'].shape.as_list())
def test_sync_bn_multiple_devices(self, strategy, use_sync_bn): """Test for sync bn on TPU and GPU devices.""" inputs = np.random.rand(1, 224, 224, 3) tf.keras.backend.set_image_data_format('channels_last') with strategy.scope(): network = darknet.Darknet(model_id='darknet53', min_size=3, max_size=5) _ = network(inputs)
def test_input_specs(self, input_dim): """Test different input feature dimensions.""" tf.keras.backend.set_image_data_format('channels_last') input_specs = tf.keras.layers.InputSpec(shape=[None, None, None, input_dim]) network = darknet.Darknet( model_id='darknet53', min_level=3, max_level=5, input_specs=input_specs) inputs = tf.keras.Input(shape=(224, 224, input_dim), batch_size=1) _ = network(inputs)