def testNesting(self):
with context.graph_mode():
inner = data_structures.List()
outer = data_structures.List([inner])
inner.append(non_keras_core.Dense(1))
inner[0](array_ops.ones([2, 3]))
self.assertEqual(2, len(outer.variables))
self.assertIsInstance(outer.variables[0],
resource_variable_ops.ResourceVariable)
def __init__(self):
super(HasMapping, self).__init__()
self.layer_dict = data_structures.Mapping(output=core.Dense(7))
self.layer_dict["norm"] = data_structures.List()
self.layer_dict["dense"] = data_structures.List()
self.layer_dict["dense"].extend(
[core.Dense(5),
core.Dense(6, kernel_regularizer=tf.reduce_sum)])
self.layer_dict["norm"].append(normalization.BatchNormalization())
self.layer_dict["norm"].append(normalization.BatchNormalization())
def testNoOverwrite(self):
mapping = data_structures.Mapping()
original = data_structures.List()
mapping["a"] = original
with self.assertRaises(ValueError):
mapping["a"] = data_structures.List()
self.assertIs(original, mapping["a"])
with self.assertRaises(AttributeError):
del mapping["a"]
mapping.update(b=data_structures.Mapping())
with self.assertRaises(ValueError):
mapping.update({"b": data_structures.Mapping()})
def testNoOverwrite(self):
mapping = data_structures.Mapping()
original = data_structures.List()
mapping["a"] = original
with self.assertRaises(ValueError):
mapping["a"] = data_structures.List()
self.assertIs(original, mapping["a"])
with self.assertRaises(AttributeError):
del mapping["a"] # pylint: disable=unsupported-delete-operation
mapping.update(b=data_structures.Mapping())
with self.assertRaises(ValueError):
mapping.update({"b": data_structures.Mapping()})
def testListWrapperBasic(self):
# _ListWrapper, unlike List, compares like the built-in list type (since it
# is used to automatically replace lists).
a = tracking.AutoTrackable()
b = tracking.AutoTrackable()
self.assertEqual([a, a], [a, a])
self.assertEqual(data_structures._ListWrapper([a, a]),
data_structures._ListWrapper([a, a]))
self.assertEqual([a, a], data_structures._ListWrapper([a, a]))
self.assertEqual(data_structures._ListWrapper([a, a]), [a, a])
self.assertNotEqual([a, a], [b, a])
self.assertNotEqual(data_structures._ListWrapper([a, a]),
data_structures._ListWrapper([b, a]))
self.assertNotEqual([a, a], data_structures._ListWrapper([b, a]))
self.assertLess([a], [a, b])
self.assertLess(data_structures._ListWrapper([a]),
data_structures._ListWrapper([a, b]))
self.assertLessEqual([a], [a, b])
self.assertLessEqual(data_structures._ListWrapper([a]),
data_structures._ListWrapper([a, b]))
self.assertGreater([a, b], [a])
self.assertGreater(data_structures._ListWrapper([a, b]),
data_structures._ListWrapper([a]))
self.assertGreaterEqual([a, b], [a])
self.assertGreaterEqual(data_structures._ListWrapper([a, b]),
data_structures._ListWrapper([a]))
self.assertEqual([a], data_structures._ListWrapper([a]))
self.assertEqual([a], list(data_structures.List([a])))
self.assertEqual([a, a], data_structures._ListWrapper([a]) + [a])
self.assertEqual([a, a], [a] + data_structures._ListWrapper([a]))
self.assertIsInstance(data_structures._ListWrapper([a]), list)
def __init__(self):
super(HasList, self).__init__()
self.layer_list = data_structures.List([core.Dense(3)])
self.layer_list.append(core.Dense(4))
self.layer_list.extend(
[core.Dense(5),
core.Dense(6, kernel_regularizer=tf.reduce_sum)])
self.layer_list += [
core.Dense(7, bias_regularizer=tf.reduce_sum),
core.Dense(8)
]
self.layer_list += (data_structures.List([core.Dense(9)]) +
data_structures.List([core.Dense(10)]))
self.layer_list.extend(
data_structures.List(list([core.Dense(11)]) + [core.Dense(12)]))
self.layers_with_updates = data_structures.List(
(normalization.BatchNormalization(), ))
def testDictWrapperBadKeys(self):
a = tracking.AutoTrackable()
a.d = {}
a.d[1] = data_structures.List()
model = training.Model()
model.sub = a
save_path = os.path.join(self.get_temp_dir(), "ckpt")
with self.assertRaisesRegexp(ValueError, "non-string key"):
model.save_weights(save_path)
def testCopy(self):
v1 = resource_variable_ops.ResourceVariable(1.)
v2 = resource_variable_ops.ResourceVariable(1.)
v3 = resource_variable_ops.ResourceVariable(1.)
l1 = data_structures.List([v1, v2])
l2 = l1.copy()
l2.append(v3)
self.assertEqual(list(l1), [v1, v2])
self.assertEqual(list(l2), [v1, v2, v3])
def testSlicing(self):
v1 = resource_variable_ops.ResourceVariable(1.)
v2 = resource_variable_ops.ResourceVariable(1.)
v3 = resource_variable_ops.ResourceVariable(1.)
v4 = resource_variable_ops.ResourceVariable(1.)
l = data_structures.List([v1, v2, v3, v4])
self.assertEqual(l[1:], [v2, v3, v4])
self.assertEqual(l[1:-1], [v2, v3])
self.assertEqual(l[:-1], [v1, v2, v3])
def testNonLayerVariables(self):
v = resource_variable_ops.ResourceVariable([1.])
l = data_structures.List([v])
self.assertTrue(l.trainable)
self.assertEqual([], l.layers)
self.assertEqual([v], l.variables)
self.assertEqual([v], l.trainable_weights)
self.assertEqual([], l.non_trainable_variables)
l.trainable = False
self.assertEqual([v], l.variables)
self.assertEqual([], l.trainable_variables)
self.assertEqual([v], l.non_trainable_variables)
l.trainable = True
v2 = resource_variable_ops.ResourceVariable(1., trainable=False)
l.append(v2)
self.assertEqual([v, v2], l.weights)
self.assertEqual([v], l.trainable_weights)
self.assertEqual([v2], l.non_trainable_weights)
def testNonStringKeys(self):
mapping = data_structures.Mapping()
with self.assertRaises(TypeError):
mapping[1] = data_structures.List()
def testNotTrackable(self):
class NotTrackable(object):
pass
with self.assertRaises(ValueError):
data_structures.List([NotTrackable()])
def testCallNotImplemented(self):
with self.assertRaisesRegexp(TypeError, "not callable"):
data_structures.List()(1.)
def testRMul(self):
v = resource_variable_ops.ResourceVariable(1.)
l = data_structures.List([v, v, v])
self.assertEqual(list(2 * l), [v, v, v] * 2)
def testIMul(self):
v = resource_variable_ops.ResourceVariable(1.)
l = data_structures.List([v])
l *= 2
self.assertEqual(list(l), [v] * 2)
def testIMul_zero(self):
l = data_structures.List([])
with self.assertRaisesRegexp(ValueError, "List only supports append"):
l *= 0
def testHash(self):
has_sequences = set([data_structures.List(), data_structures.List()])
self.assertEqual(2, len(has_sequences))
self.assertNotIn(data_structures.List(), has_sequences)
def testNoPop(self):
with self.assertRaises(AttributeError):
data_structures.List().pop()
