def test_to_representation_for_type_with_tensor_type(self):
self.assertEqual(
reference_executor.to_representation_for_type(10, tf.int32), 10)
with self.assertRaises(TypeError):
reference_executor.to_representation_for_type(0.1, tf.int32)
with self.assertRaises(TypeError):
reference_executor.to_representation_for_type([], tf.int32)
def test_to_representation_for_type_with_federated_type(self):
self.assertEqual(
reference_executor.to_representation_for_type(
10,
computation_types.FederatedType(
tf.int32, placements.SERVER, all_equal=True)), 10)
x = [1, 2, 3]
self.assertEqual(
reference_executor.to_representation_for_type(
x,
computation_types.FederatedType(
tf.int32, placements.CLIENTS, all_equal=False)), x)
def test_to_representation_for_type_with_function_type(self):
def foo(x):
self.assertIsInstance(x, reference_executor.ComputedValue)
return reference_executor.ComputedValue(str(x.value), tf.string)
self.assertIs(
reference_executor.to_representation_for_type(
foo, computation_types.FunctionType(tf.int32, tf.string),
lambda x, t: x), foo)
with self.assertRaises(TypeError):
reference_executor.to_representation_for_type(
foo, computation_types.FunctionType(tf.int32, tf.string))
with self.assertRaises(TypeError):
reference_executor.to_representation_for_type(
10, computation_types.FunctionType(tf.int32, tf.string))
def test_to_representation_for_type_with_named_tuple_type(self):
foo = anonymous_tuple.AnonymousTuple([('x', 10), ('y', 20)])
self.assertEqual(
reference_executor.to_representation_for_type(foo, [('x', tf.int32),
('y', tf.int32)]),
foo)
foo = anonymous_tuple.AnonymousTuple([
('x', anonymous_tuple.AnonymousTuple([(None, 10), (None, 20)])),
('y', 30),
])
self.assertEqual(
reference_executor.to_representation_for_type(
foo, [('x', [tf.int32, tf.int32]), ('y', tf.int32)]), foo)
self.assertEqual(
reference_executor.to_representation_for_type(
anonymous_tuple.AnonymousTuple([('x', [10, 20]), ('y', 30)]),
[('x', [tf.int32, tf.int32]), ('y', tf.int32)]),
anonymous_tuple.AnonymousTuple([('x',
anonymous_tuple.AnonymousTuple(
[(None, 10), (None, 20)])),
('y', 30)]))
with self.assertRaises(TypeError):
reference_executor.to_representation_for_type(10, [tf.int32, tf.int32])
unordered_dict = {'a': 10, 'b': 20}
self.assertEqual(
str(
reference_executor.to_representation_for_type(
unordered_dict, [('a', tf.int32), ('b', tf.int32)])),
'<a=10,b=20>')
self.assertEqual(
str(
reference_executor.to_representation_for_type(
unordered_dict, [('b', tf.int32), ('a', tf.int32)])),
'<b=20,a=10>')
def test_stamp_computed_value_into_graph_with_tuples_of_tensors(self):
v_val = anonymous_tuple.AnonymousTuple([('x', 10),
('y',
anonymous_tuple.AnonymousTuple(
[('z', 0.6)]))])
v_type = [('x', tf.int32), ('y', [('z', tf.float32)])]
v = reference_executor.ComputedValue(
reference_executor.to_representation_for_type(v_val, v_type), v_type)
with tf.Graph().as_default() as graph:
stamped_v = reference_executor.stamp_computed_value_into_graph(v, graph)
with tf.Session(graph=graph) as sess:
v_val = graph_utils.fetch_value_in_session(sess, stamped_v)
self.assertEqual(str(v_val), '<x=10,y=<z=0.6>>')
def test_to_representation_for_type_with_sequence_type(self):
foo = [1, 2, 3]
self.assertEqual(
reference_executor.to_representation_for_type(
foo, computation_types.SequenceType(tf.int32)), foo)
def test_to_representation_for_type_with_placement_type(self):
self.assertIs(
reference_executor.to_representation_for_type(
placements.CLIENTS, computation_types.PlacementType()),
placements.CLIENTS)
def test_to_representation_for_type_with_abstract_type(self):
with self.assertRaises(TypeError):
reference_executor.to_representation_for_type(
10, computation_types.AbstractType('T'))
