def test_functions_have_same_trace(self):
class Layer(keras.engine.base_layer.Layer):
def call(self, inputs):
return inputs
def call2(self, inputs):
return inputs * 2
layer = Layer()
call_collection = keras_save.LayerCallCollection(layer)
fn = call_collection.add_function(layer.call, 'call', True)
fn2 = call_collection.add_function(layer.call2, 'call2', True)
with keras_save.tracing_scope():
fn(np.ones((2, 3)))
fn(np.ones((4, 5)))
self.assertLen(
fn.wrapped_call._list_all_concrete_functions_for_serialization(), 2)
self.assertLen(
fn2.wrapped_call._list_all_concrete_functions_for_serialization(), 2)
# Check that the shapes are correct
self.assertEqual(
{(2, 3), (4, 5)},
set(tuple(c.structured_input_signature[0][0].shape.as_list()) for c in
fn2.wrapped_call._list_all_concrete_functions_for_serialization()))
def test_maintains_losses(self):
layer = LayerWithLoss()
layer(np.ones((2, 3)))
previous_losses = layer.losses[:]
call_collection = keras_save.LayerCallCollection(layer)
fn = call_collection.add_function(layer.call, 'call', True)
fn(np.ones((2, 3)))
self.assertAllEqual(previous_losses, layer.losses)
def assert_num_traces(layer_cls, training_keyword):
layer = layer_cls()
call_collection = keras_save.LayerCallCollection(layer)
fn = call_collection.add_function(layer.call, 'call')
fn(np.ones((2, 3)), training=True)
self.assertLen(fn._list_all_concrete_functions_for_serialization(), 2)
fn(np.ones((2, 4)), training=False)
self.assertLen(fn._list_all_concrete_functions_for_serialization(), 4)
if training_keyword:
fn(np.ones((2, 5)), True)
self.assertLen(fn._list_all_concrete_functions_for_serialization(), 6)
fn(np.ones((2, 6)))
self.assertLen(fn._list_all_concrete_functions_for_serialization(), 8)
