def test_encode_decode_type_spec(self):
spec = tensor_spec.TensorSpec((1, 5), dtypes.float32)
string = json_utils.Encoder().encode(spec)
loaded = json_utils.decode(string)
self.assertEqual(spec, loaded)
invalid_type_spec = {
'class_name': 'TypeSpec',
'type_spec': 'Invalid Type',
'serialized': None
}
string = json_utils.Encoder().encode(invalid_type_spec)
with self.assertRaisesRegexp(ValueError,
'No TypeSpec has been registered'):
loaded = json_utils.decode(string)
def test_encode_decode_tuple(self):
metadata = {'key1': (3, 5), 'key2': [(1, (3, 4)), (1, )]}
string = json_utils.Encoder().encode(metadata)
loaded = json_utils.decode(string)
self.assertEqual(set(loaded.keys()), {'key1', 'key2'})
self.assertAllEqual(loaded['key1'], (3, 5))
self.assertAllEqual(loaded['key2'], [(1, (3, 4)), (1, )])
def tracking_metadata(self):
"""String stored in metadata field in the SavedModel proto.
Returns:
A serialized JSON storing information necessary for recreating this layer.
"""
# TODO(kathywu): check that serialized JSON can be loaded (e.g., if an
# object is in the python property)
return json_utils.Encoder().encode(self.python_properties)
def test_encode_decode_enum(self):
class Enum(enum.Enum):
CLASS_A = 'a'
CLASS_B = 'b'
config = {'key': Enum.CLASS_A, 'key2': Enum.CLASS_B}
string = json_utils.Encoder().encode(config)
loaded = json_utils.decode(string)
self.assertAllEqual({'key': 'a', 'key2': 'b'}, loaded)
def test_encode_decode_tensor_shape(self):
metadata = {
'key1':
tensor_shape.TensorShape(None),
'key2': [
tensor_shape.TensorShape([None]),
tensor_shape.TensorShape([3, None, 5])
]
}
string = json_utils.Encoder().encode(metadata)
loaded = json_utils.decode(string)
self.assertEqual(set(loaded.keys()), {'key1', 'key2'})
self.assertAllEqual(loaded['key1'].rank, None)
self.assertAllEqual(loaded['key2'][0].as_list(), [None])
self.assertAllEqual(loaded['key2'][1].as_list(), [3, None, 5])
