def test_variables_are_not_enumerated_when_overridden(self):
layer = signal_conv.SignalConv2D(1, 1)
layer.kernel_parameter = [[[[1]]]]
layer.bias_parameter = [0]
layer.build((None, None, None, 1))
self.assertEmpty(layer.weights)
self.assertEmpty(layer.trainable_weights)
def test_variables_are_enumerated(self):
layer = signal_conv.SignalConv2D(3, 1, use_bias=True)
layer.build((None, None, None, 2))
self.assertLen(layer.weights, 2)
self.assertLen(layer.trainable_weights, 2)
weight_names = [w.name for w in layer.weights]
self.assertSameElements(weight_names, ["kernel_rdft:0", "bias:0"])
def test_bias_variable_is_not_unnecessarily_created(self):
layer = signal_conv.SignalConv2D(5, 3, use_bias=False)
layer.build((None, None, None, 3))
self.assertLen(layer.weights, 1)
self.assertLen(layer.trainable_weights, 1)
weight_names = [w.name for w in layer.weights]
self.assertSameElements(weight_names, ["kernel_rdft:0"])
def test_dtypes_are_correct_with_mixed_precision(self):
tf.keras.mixed_precision.set_global_policy("mixed_float16")
try:
x = tf.random.uniform((1, 4, 4, 3), dtype=tf.float16)
layer = signal_conv.SignalConv2D(2, 3, use_bias=True)
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_trainable_state_follows_layer(self):
layer = signal_conv.SignalConv2D(1, 1, use_bias=True)
layer.trainable = False
layer.build((None, None, None, 1))
self.assertLen(layer.weights, 2)
self.assertEmpty(layer.trainable_weights)
