def test_vector_input_raises_error(self): x = tf.random.uniform((3, ), dtype=tf.float32) with self.assertRaises(ValueError): gdn.GDN(inverse=False, rectify=False, data_format="channels_last")(x) with self.assertRaises(ValueError): gdn.GDN(inverse=True, rectify=True, data_format="channels_first")(x)
def test_variables_are_not_enumerated_when_overridden(self): layer = gdn.GDN() layer.beta_parameter = [1] layer.gamma_parameter = [.1] layer.build((None, 1)) self.assertEmpty(layer.weights) self.assertEmpty(layer.trainable_weights)
def test_channels_first_has_correct_output(self, rank): # This tests that the layer produces the correct output for a number of # different input dimensionalities with 'channels_first' data format. x = tf.random.uniform((6, 5, 4, 3, 2, 1)[:rank], dtype=tf.float32) y = gdn.GDN(inverse=False, rectify=False, data_format="channels_first")(x) self.assertEqual(x.shape, y.shape) self.assertAllClose(y, x / (1 + .1 * abs(x)), rtol=0, atol=1e-6)
def test_quadratic_gdn_has_correct_output(self): x = tf.random.uniform((1, 2, 3, 4), -.5, .5, dtype=tf.float32) y = gdn.GDN( inverse=False, rectify=False, alpha_parameter=2, epsilon_parameter=.5)(x) self.assertEqual(x.shape, y.shape) self.assertAllClose(y, x / tf.sqrt(1 + .1 * (x ** 2)), rtol=0, atol=1e-6)
def test_attributes_cannot_be_set_after_build(self): layer = gdn.GDN() layer.build((None, 2)) with self.assertRaises(RuntimeError): layer.inverse = True with self.assertRaises(RuntimeError): layer.rectify = True with self.assertRaises(RuntimeError): layer.data_format = "channels_first" with self.assertRaises(RuntimeError): layer.alpha_parameter = 5 with self.assertRaises(RuntimeError): layer.beta_parameter = tf.ones((5, )) with self.assertRaises(RuntimeError): layer.gamma_parameter = tf.ones((5, 5)) with self.assertRaises(RuntimeError): layer.epsilon_parameter = 1 / 3 with self.assertRaises(RuntimeError): layer.alpha_initializer = "ones" with self.assertRaises(RuntimeError): layer.beta_initializer = tf.keras.initializers.Ones() with self.assertRaises(RuntimeError): layer.gamma_initializer = tf.keras.initializers.Ones() with self.assertRaises(RuntimeError): layer.epsilon_initializer = "ones"
def test_variables_receive_gradients(self): x = tf.random.uniform((1, 2), dtype=tf.float32) layer = gdn.GDN(inverse=False, rectify=True) with tf.GradientTape() as g: y = layer(x) grads = g.gradient(y, layer.trainable_variables) self.assertLen(grads, 2) self.assertNotIn(None, grads)
def test_fixed_gdn_has_correct_output(self): x = tf.random.uniform((10, 3), -.5, .5, dtype=tf.float32) y = gdn.GDN( inverse=False, rectify=False, beta_parameter=[0, 0, 0], gamma_parameter=tf.ones((3, 3)))(x) self.assertEqual(x.shape, y.shape) expected_y = x / tf.reduce_sum(abs(x), axis=-1, keepdims=True) self.assertAllClose(y, expected_y, rtol=0, atol=1e-6)
def _run_gdn(self, x, shape, inverse, rectify, data_format): inputs = tf.placeholder(tf.float32, shape) layer = gdn.GDN( inverse=inverse, rectify=rectify, data_format=data_format) outputs = layer(inputs) with self.test_session() as sess: tf.global_variables_initializer().run() y, = sess.run([outputs], {inputs: x}) return y
def test_dtypes_are_correct_with_mixed_precision(self): tf.keras.mixed_precision.set_global_policy("mixed_float16") try: x = tf.random.uniform((4, 3), dtype=tf.float16) layer = gdn.GDN() y = layer(x) for variable in layer.variables: self.assertEqual(variable.dtype, tf.float32) self.assertEqual(y.dtype, tf.float16) finally: tf.keras.mixed_precision.set_global_policy(None)
def test_variables_receive_gradients(self): x = tf.random.uniform((1, 2), dtype=tf.float32) layer = gdn.GDN(inverse=False, rectify=True) with tf.GradientTape() as g: y = layer(x) grads = g.gradient(y, layer.trainable_weights) self.assertLen(grads, 2) self.assertNotIn(None, grads) grad_shapes = [tuple(g.shape) for g in grads] weight_shapes = [tuple(w.shape) for w in layer.trainable_weights] self.assertSameElements(grad_shapes, weight_shapes)
def test_variables_are_enumerated(self): layer = gdn.GDN() layer.alpha_parameter = None layer.epsilon_parameter = None layer.build((None, 5)) self.assertLen(layer.weights, 4) self.assertLen(layer.trainable_weights, 4) weight_names = [w.name for w in layer.weights] self.assertSameElements(weight_names, [ "reparam_alpha:0", "reparam_beta:0", "reparam_gamma:0", "reparam_epsilon:0"])
def test_channels_last_has_correct_output(self): # This tests that the layer produces the correct output for a number of # different input dimensionalities with 'channels_last' data format. for ndim in [2, 3, 4, 5, 6]: x = tf.random.uniform((1, 2, 3, 4, 5, 6)[:ndim], dtype=tf.float32) y = gdn.GDN(inverse=False, rectify=False, data_format="channels_last")(x) self.assertEqual(x.shape, y.shape) self.assertAllClose(y, x / tf.sqrt(1 + .1 * (x**2)), rtol=0, atol=1e-6)
def test_can_be_saved_within_functional_model(self, build): inputs = tf.keras.Input(shape=(5, )) outputs = gdn.GDN()(inputs) model = tf.keras.Model(inputs=inputs, outputs=outputs) layer = model.get_layer("gdn") with self.subTest(name="layer_created_as_expected"): self.assertIsInstance(layer, gdn.GDN) self.assertIsInstance(layer.alpha_parameter, tf.Tensor) self.assertEmpty(layer.alpha_parameter.shape) self.assertIsInstance(layer.beta_parameter, parameters.GDNParameter) self.assertIsInstance(layer.gamma_parameter, parameters.GDNParameter) self.assertIsInstance(layer.epsilon_parameter, tf.Tensor) self.assertEmpty(layer.epsilon_parameter.shape) if build: x = tf.random.uniform((5, 5), dtype=tf.float32) y = model(x) weight_names = [w.name for w in model.weights] tempdir = self.create_tempdir() model_path = os.path.join(tempdir, "model") # This should force the model to be reconstructed via configs. model.save(model_path, save_traces=False) model = tf.keras.models.load_model(model_path) layer = model.get_layer("gdn") with self.subTest(name="layer_recreated_as_expected"): self.assertIsInstance(layer, gdn.GDN) self.assertIsInstance(layer.alpha_parameter, tf.Tensor) self.assertEmpty(layer.alpha_parameter.shape) self.assertIsInstance(layer.beta_parameter, parameters.GDNParameter) self.assertIsInstance(layer.gamma_parameter, parameters.GDNParameter) self.assertIsInstance(layer.epsilon_parameter, tf.Tensor) self.assertEmpty(layer.epsilon_parameter.shape) if build: with self.subTest(name="model_outputs_identical"): self.assertAllEqual(model(x), y) with self.subTest(name="model_weights_identical"): self.assertSameElements(weight_names, [w.name for w in model.weights])
def test_rgdn_has_correct_output(self): x = tf.random.uniform((1, 2, 3, 4), -.5, .5, dtype=tf.float32) y = gdn.GDN(inverse=False, rectify=True)(x) self.assertEqual(x.shape, y.shape) x = tf.maximum(x, 0) self.assertAllClose(y, x / (1 + .1 * x), rtol=0, atol=1e-6)
def test_igdn_has_correct_output(self): x = tf.random.uniform((1, 2, 3, 4), dtype=tf.float32) y = gdn.GDN(inverse=True, rectify=False)(x) self.assertEqual(x.shape, y.shape) self.assertAllClose(y, x * (1 + .1 * abs(x)), rtol=0, atol=1e-6)
def test_vector_input_raises_error(self, data_format): x = tf.zeros((3, ), dtype=tf.float32) with self.assertRaises(ValueError): gdn.GDN(data_format=data_format)(x)
def test_invalid_data_format_raises_error(self): with self.assertRaises(ValueError): gdn.GDN(data_format="NHWC")
def test_variables_trainable_state_follows_layer(self): layer = gdn.GDN() layer.trainable = False layer.build((None, 1)) self.assertLen(layer.weights, 2) self.assertEmpty(layer.trainable_weights)
def test_invalid_data_format_raises_error(self): x = tf.random.uniform((1, 2, 3, 4), dtype=tf.float32) with self.assertRaises(ValueError): gdn.GDN(inverse=False, rectify=False, data_format="NHWC")(x)