def _assert_successful_conversion(
prot,
graph_def,
actual,
*input_fns,
decimals=3,
**kwargs, # pylint: disable=unused-argument
):
converter = Converter(
registry(),
config=tfe.get_config(),
protocol=prot,
model_provider="model-provider",
)
x = converter.convert(graph_def, "input-provider", list(input_fns))
with tfe.Session() as sess:
sess.run(tf.global_variables_initializer())
if not isinstance(x, (list, tuple)):
x = [x]
actual = [actual]
else:
assert isinstance(
actual, (list, tuple)
), "expected output to be tensor sequence"
try:
output = sess.run([xi.reveal().decode() for xi in x], tag="reveal")
except AttributeError:
# assume all xi are all public
output = sess.run(x, tag="reveal")
for o_i, a_i in zip(output, actual):
np.testing.assert_array_almost_equal(o_i, a_i, decimal=decimals)
def _assert_successful_conversion(prot,
graph_def,
actual,
*input_fns,
decimals=3,
**kwargs):
prot.clear_initializers()
converter = Converter(tfe.get_config(), prot, 'model-provider')
x = converter.convert(graph_def, registry(), 'input-provider',
list(input_fns))
with tfe.Session() as sess:
sess.run(tf.global_variables_initializer())
if not isinstance(x, (list, tuple)):
x = [x]
actual = [actual]
else:
assert isinstance(
actual,
(list, tuple)), "expected output to be tensor sequence"
try:
output = sess.run([xi.reveal().decode() for xi in x],
tag='reveal')
except AttributeError:
# assume all xi are all public
output = sess.run([xi for xi in x], tag='reveal')
for o_i, a_i in zip(output, actual):
np.testing.assert_array_almost_equal(o_i,
a_i,
decimal=decimals)
def test_empty_model(self):
test_input = np.ones([1, 8, 8, 1])
graph_def, prot_class = self._construct_empty_conversion_test(
'empty_model', protocol='SecureNN')
with prot_class() as prot:
input_fn = self.ndarray_input_fn(test_input)
prot.clear_initializers()
converter = Converter(
registry(),
config=tfe.get_config(),
protocol=prot,
model_provider='model-provider',
)
self.assertRaises(ValueError, converter.convert, graph_def,
'input-provider', input_fn)
def test_empty_model(self):
test_input = np.ones([1, 8, 8, 1])
graph_def, prot_class = self._construct_empty_conversion_test(
"empty_model", protocol="SecureNN"
)
with prot_class() as prot:
input_fn = self.ndarray_input_fn(test_input)
converter = Converter(
registry(),
config=tfe.get_config(),
protocol=prot,
model_provider="model-provider",
)
self.assertRaises(
ValueError, converter.convert, graph_def, "input-provider", input_fn,
)
'server0', 'server1', 'crypto-producer', 'prediction-client',
'weights-provider'
])
def provide_input() -> tf.Tensor:
return tf.constant(np.random.normal(size=(1, 1, 28, 28)), tf.float32)
def receive_output(tensor: tf.Tensor) -> tf.Tensor:
tf.print(tensor, [tensor])
return tensor
with tfe.protocol.Pond(
*config.get_players('server0, server1, crypto-producer')) as prot:
c = convert.Converter(config, prot, config.get_player('weights-provider'))
x = c.convert(graph_def, registry(),
config.get_player('prediction-client'), provide_input)
prediction_op = prot.define_output(config.get_player('prediction-client'),
x, receive_output)
with tfe.Session(config=config) as sess:
sess.run(tfe.global_variables_initializer(), tag='init')
sess.run(prediction_op, tag='prediction')
os.remove(model_filename)