def test_l1_with_weight(self):
composite = losses.CompositeLoss()
composite.add_loss('l1', 2.0)
y_true = tf.constant(0.3, shape=(2, 192, 256, 3), dtype=tf.float32)
y_pred = tf.constant(0.5, shape=(2, 192, 256, 3), dtype=tf.float32)
self.assertAllClose(composite(y_true, y_pred),
tf.reduce_mean(tf.abs(y_true - y_pred)) * 2.0)
def test_l1_l2_different_weights(self):
composite = losses.CompositeLoss()
composite.add_loss('L1', 1.0)
composite.add_loss('L2', 0.5)
y_true = tf.constant(127, shape=(2, 192, 256, 3), dtype=tf.int32)
y_pred = tf.constant(215, shape=(2, 192, 256, 3), dtype=tf.int32)
l1 = tf.cast(tf.reduce_mean(tf.abs(y_true - y_pred)), tf.float32)
l2 = tf.cast(tf.reduce_mean(tf.square(y_true - y_pred)), tf.float32)
self.assertAllClose(composite(y_true, y_pred), l1 * 1.0 + l2 * 0.5)
def test_composite_loss_equals_sum_of_components(self):
composite = losses.CompositeLoss()
mae = tf.keras.losses.MAE
vgg = losses.PerceptualLoss()
composite.add_loss(mae, 1.0)
composite.add_loss(vgg, 2.0)
y_true = tf.random.uniform((1, 192, 256, 3))
y_pred = tf.random.uniform((1, 192, 256, 3))
loss_value = composite(y_true, y_pred)
mae_loss_value = tf.math.reduce_mean(mae(y_true, y_pred))
vgg_loss_value = vgg(y_true, y_pred)
self.assertAllClose(loss_value,
mae_loss_value * 1.0 + vgg_loss_value * 2.0)
def test_invalid_weight(self):
composite = losses.CompositeLoss()
with self.assertRaisesRegex(ValueError, r'-1\.0'):
composite.add_loss('l2', -1.0)
def test_call_before_adding_component_raises_error(self):
composite = losses.CompositeLoss()
y_true = tf.random.uniform((1, 192, 256, 3))
y_pred = tf.random.uniform((1, 192, 256, 3))
with self.assertRaises(AssertionError):
composite(y_true, y_pred)
def test_duplicate_component_raises_error(self):
composite = losses.CompositeLoss()
composite.add_loss('l1', 1.0)
with self.assertRaisesRegex(ValueError, 'exist'):
composite.add_loss('l1', 2.0)