def test_weights():
# Check if weights get initialized correctly
layer = GroupNormalization(groups=1, scale=False, center=False)
layer.build((None, 3, 4))
assert len(layer.trainable_weights) == 0
assert len(layer.weights) == 0
layer = InstanceNormalization()
layer.build((None, 3, 4))
assert len(layer.trainable_weights) == 2
assert len(layer.weights) == 2
def test_weights(self):
# Check if weights get initialized correctly
layer = GroupNormalization(groups=1, scale=False, center=False)
layer.build((None, 3, 4))
self.assertEqual(len(layer.trainable_weights), 0)
self.assertEqual(len(layer.weights), 0)
layer = InstanceNormalization()
layer.build((None, 3, 4))
self.assertEqual(len(layer.trainable_weights), 2)
self.assertEqual(len(layer.weights), 2)
def test_regularizations(self):
layer = GroupNormalization(
gamma_regularizer='l1', beta_regularizer='l1', groups=4, axis=2)
layer.build((None, 4, 4))
self.assertEqual(len(layer.losses), 2)
max_norm = tf.keras.constraints.max_norm
layer = GroupNormalization(
gamma_constraint=max_norm, beta_constraint=max_norm)
layer.build((None, 3, 4))
self.assertEqual(layer.gamma.constraint, max_norm)
self.assertEqual(layer.beta.constraint, max_norm)
def test_regularizations():
layer = GroupNormalization(gamma_regularizer="l1",
beta_regularizer="l1",
groups=4,
axis=2)
layer.build((None, 4, 4))
assert len(layer.losses) == 2
max_norm = tf.keras.constraints.max_norm
layer = GroupNormalization(gamma_constraint=max_norm,
beta_constraint=max_norm)
layer.build((None, 3, 4))
assert layer.gamma.constraint == max_norm
assert layer.beta.constraint == max_norm
def test_group_norm_compute_output_shape(center, scale):
target_variables_len = [center, scale].count(True)
target_trainable_variables_len = [center, scale].count(True)
layer1 = GroupNormalization(groups=2, center=center, scale=scale)
layer1.build(input_shape=[8, 28, 28, 16]) # build()
assert len(layer1.variables) == target_variables_len
assert len(layer1.trainable_variables) == target_trainable_variables_len
layer2 = GroupNormalization(groups=2, center=center, scale=scale)
layer2.compute_output_shape(input_shape=[8, 28, 28,
16]) # compute_output_shape()
assert len(layer2.variables) == target_variables_len
assert len(layer2.trainable_variables) == target_trainable_variables_len
layer3 = GroupNormalization(groups=2, center=center, scale=scale)
layer3(tf.random.normal(shape=[8, 28, 28, 16])) # call()
assert len(layer3.variables) == target_variables_len
assert len(layer3.trainable_variables) == target_trainable_variables_len
