def test_dict_to_example(self):
example = file_format_adapter._dict_to_tf_example({
"a":
1,
"a2":
np.array(1),
"b": ["foo", "bar"],
"b2":
np.array(["foo", "bar"]),
"c": [2.0],
"c2":
np.array([2.0]),
# Empty values supported when type is defined
"d":
np.array([], dtype=np.int32),
# Support for byte strings
"e":
np.zeros(2, dtype=np.uint8).tobytes(),
"e2": [np.zeros(2, dtype=np.uint8).tobytes()] * 2,
})
feature = example.features.feature
self.assertEqual([1], list(feature["a"].int64_list.value))
self.assertEqual([1], list(feature["a2"].int64_list.value))
self.assertEqual([b"foo", b"bar"], list(feature["b"].bytes_list.value))
self.assertEqual([b"foo", b"bar"],
list(feature["b2"].bytes_list.value))
self.assertEqual([2.0], list(feature["c"].float_list.value))
self.assertEqual([2.0], list(feature["c2"].float_list.value))
self.assertEqual([], list(feature["d"].int64_list.value))
self.assertEqual([b"\x00\x00"], list(feature["e"].bytes_list.value))
self.assertEqual([b"\x00\x00", b"\x00\x00"],
list(feature["e2"].bytes_list.value))
with self.assertRaisesWithPredicateMatch(ValueError,
"Received an empty"):
# Raise error if an undefined empty value is given
file_format_adapter._dict_to_tf_example({
"empty": [],
})
with self.assertRaisesWithPredicateMatch(ValueError,
"not support type"):
# Raise error if an unsupported dtype is given
file_format_adapter._dict_to_tf_example({
"wrong_type":
np.zeros(shape=(5, ), dtype=np.complex64),
})
def test_convert_to_example_generator(self):
wrapped = file_format_adapter._generate_tf_examples(self.generator())
expected = file_format_adapter._dict_to_tf_example(self.example_dict)
wrapped_examples = list(wrapped)
self.assertEqual(3, len(wrapped_examples))
for serialized_example in wrapped_examples:
example = tf.train.Example()
example.ParseFromString(serialized_example)
self.assertEqual(expected, example)
def test_dict_to_example(self):
example = file_format_adapter._dict_to_tf_example({
"a": 1,
"b": ["foo", "bar"],
"c": [2.0],
})
feature = example.features.feature
self.assertEqual([1], list(feature["a"].int64_list.value))
self.assertEqual([b"foo", b"bar"], list(feature["b"].bytes_list.value))
self.assertEqual([2.0], list(feature["c"].float_list.value))