def test_roundtrip_sequence_of_scalars(self):
x = tf.data.Dataset.range(5).map(lambda x: x * 2)
serialized_bytes = executor_serialization._serialize_dataset(x)
y = executor_serialization._deserialize_dataset(serialized_bytes)
self.assertEqual(x.element_spec, y.element_spec)
self.assertAllEqual(list(y), [x * 2 for x in range(5)])
def test_roundtrip_sequence_of_tuples(self):
x = tf.data.Dataset.range(5).map(lambda x: (x * 2, tf.cast(
x, tf.int32), tf.cast(x - 1, tf.float32)))
serialized_bytes = executor_serialization._serialize_dataset(x)
y = executor_serialization._deserialize_dataset_from_graph_def(
serialized_bytes,
element_type=computation_types.to_type(x.element_spec))
self.assertEqual(x.element_spec, y.element_spec)
self.assertAllEqual(list(y), [(x * 2, x, x - 1.) for x in range(5)])
def test_roundtrip_sequence_of_singleton_tuples(self):
x = tf.data.Dataset.range(5).map(lambda x: (x, ))
serialized_bytes = executor_serialization._serialize_dataset(x)
y = executor_serialization._deserialize_dataset(serialized_bytes)
self.assertEqual(x.element_spec, y.element_spec)
expected_values = [(x, ) for x in range(5)]
actual_values = self.evaluate(list(y))
self.assertAllEqual(expected_values, actual_values)
def test_roundtrip_sequence_of_tuples(self):
x = tf.data.Dataset.range(5).map(lambda x: (x * 2, tf.cast(
x, tf.int32), tf.cast(x - 1, tf.float32)))
serialized_bytes = executor_serialization._serialize_dataset(x)
y = executor_serialization._deserialize_dataset(serialized_bytes)
self.assertEqual(x.element_spec, y.element_spec)
self.assertAllEqual(self.evaluate(list(y)),
[(x * 2, x, x - 1.) for x in range(5)])
def test_roundtrip_sequence_of_singleton_tuples(self):
x = tf.data.Dataset.range(5).map(lambda x: (x, ))
serialized_bytes = executor_serialization._serialize_dataset(x)
y = executor_serialization._deserialize_dataset_from_graph_def(
serialized_bytes,
element_type=computation_types.to_type(x.element_spec))
self.assertEqual(x.element_spec, y.element_spec)
expected_values = [(x, ) for x in range(5)]
actual_values = list(y)
self.assertAllEqual(expected_values, actual_values)
def test_roundtrip_sequence_of_nested_structures(self):
test_tuple_type = collections.namedtuple('TestTuple', ['u', 'v'])
def _make_nested_tf_structure(x):
return collections.OrderedDict([
('b', tf.cast(x, tf.int32)),
('a',
tuple([
x,
test_tuple_type(x * 2, x * 3),
collections.OrderedDict([('x', x**2), ('y', x**3)])
])),
])
x = tf.data.Dataset.range(5).map(_make_nested_tf_structure)
serialzied_bytes = executor_serialization._serialize_dataset(x)
y = executor_serialization._deserialize_dataset(serialzied_bytes)
# Note: TF loses the `OrderedDict` during serialization, so the expectation
# here is for a `dict` in the result.
self.assertEqual(
y.element_spec, {
'b':
tf.TensorSpec([], tf.int32),
'a':
tuple([
tf.TensorSpec([], tf.int64),
test_tuple_type(
tf.TensorSpec([], tf.int64),
tf.TensorSpec([], tf.int64),
),
{
'x': tf.TensorSpec([], tf.int64),
'y': tf.TensorSpec([], tf.int64),
},
]),
})
def _build_expected_structure(x):
return {
'b':
x,
'a':
tuple(
[x,
test_tuple_type(x * 2, x * 3), {
'x': x**2,
'y': x**3
}])
}
actual_values = self.evaluate(list(y))
expected_values = [_build_expected_structure(x) for x in range(5)]
for actual, expected in zip(actual_values, expected_values):
self.assertAllClose(actual, expected)
def test_roundtrip_sequence_of_namedtuples(self):
test_tuple_type = collections.namedtuple('TestTuple', ['a', 'b', 'c'])
def make_test_tuple(x):
return test_tuple_type(
a=x * 2, b=tf.cast(x, tf.int32), c=tf.cast(x - 1, tf.float32))
x = tf.data.Dataset.range(5).map(make_test_tuple)
serialized_bytes = executor_serialization._serialize_dataset(x)
y = executor_serialization._deserialize_dataset_from_graph_def(
serialized_bytes,
element_type=computation_types.to_type(x.element_spec))
self.assertEqual(x.element_spec, y.element_spec)
self.assertAllEqual(
list(y), [test_tuple_type(a=x * 2, b=x, c=x - 1.) for x in range(5)])
def test_roundtrip_sequence_of_nested_structures(self):
test_tuple_type = collections.namedtuple('TestTuple', ['u', 'v'])
def _make_nested_tf_structure(x):
return collections.OrderedDict(b=tf.cast(x, tf.int32),
a=(
x,
test_tuple_type(x * 2, x * 3),
collections.OrderedDict(x=x**2,
y=x**3),
))
x = tf.data.Dataset.range(5).map(_make_nested_tf_structure)
serialzied_bytes = executor_serialization._serialize_dataset(x)
y = executor_serialization._deserialize_dataset_from_graph_def(
serialzied_bytes,
element_type=computation_types.to_type(x.element_spec))
# Note: TF loses the `OrderedDict` during serialization, so the expectation
# here is for a `dict` in the result.
expected_element_spec = collections.OrderedDict(
b=tf.TensorSpec([], tf.int32),
a=(tf.TensorSpec([], tf.int64),
test_tuple_type(tf.TensorSpec([], tf.int64),
tf.TensorSpec([], tf.int64)),
collections.OrderedDict(x=tf.TensorSpec([], tf.int64),
y=tf.TensorSpec([], tf.int64))))
self.assertEqual(y.element_spec, expected_element_spec)
def _build_expected_structure(x):
return collections.OrderedDict(b=x,
a=(
x,
test_tuple_type(x * 2, x * 3),
collections.OrderedDict(x=x**2,
y=x**3),
))
expected_values = (_build_expected_structure(x) for x in range(5))
for actual, expected in zip(y, expected_values):
self.assertAllClose(actual, expected)
def test_serialize_sequence_bytes_too_large(self):
with self.assertRaisesRegex(
ValueError, r'Serialized size .* exceeds maximum allowed'):
_ = executor_serialization._serialize_dataset(
tf.data.Dataset.range(5), max_serialized_size_bytes=0)
def test_serialize_sequence_not_a_dataset(self):
with self.assertRaisesRegex(TypeError,
r'Expected .*Dataset.* found int'):
_ = executor_serialization._serialize_dataset(5)
