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 _assert_successful_conversion(prot, graph_def, actual, *input_fns, **kwargs):
prot.clear_initializers()
converter = Converter(tfe.get_config(), prot, 'model-provider')
x = converter.convert(graph_def, register(), 'input-provider', list(input_fns))
with tfe.Session() as sess:
sess.run(tf.global_variables_initializer())
output = sess.run(x.reveal(), tag='reveal')
np.testing.assert_array_almost_equal(output, actual, decimal=3)
def test_stack_convert(self):
tf.reset_default_graph()
global global_filename
global_filename = "stack.pb"
input1 = np.array([1, 4])
input2 = np.array([2, 5])
input3 = np.array([3, 6])
path = export_stack(global_filename, input1.shape)
tf.reset_default_graph()
graph_def = read_graph(path)
tf.reset_default_graph()
actual = run_stack(input1, input2, input3)
tf.reset_default_graph()
with tfe.protocol.Pond() as prot:
prot.clear_initializers()
def provide_input1() -> tf.Tensor:
return tf.constant(input1)
def provide_input2() -> tf.Tensor:
return tf.constant(input2)
def provide_input3() -> tf.Tensor:
return tf.constant(input3)
inputs = [provide_input1, provide_input2, provide_input3]
converter = Converter(tfe.get_config(), prot, 'model-provider')
x = converter.convert(graph_def, register(), 'input-provider',
inputs)
with tfe.Session() as sess:
sess.run(prot.initializer, tag='init')
output = sess.run(x.reveal(), tag='reveal')
np.testing.assert_array_almost_equal(output, actual, decimal=3)
def test_strided_slice_convert(self):
tf.reset_default_graph()
global global_filename
global_filename = "strided_slice.pb"
path = export_strided_slice(global_filename)
tf.reset_default_graph()
graph_def = read_graph(path)
tf.reset_default_graph()
input = [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]],
[[5, 5, 5], [6, 6, 6]]]
actual = run_strided_slice(input)
tf.reset_default_graph()
with tfe.protocol.Pond() as prot:
prot.clear_initializers()
def provide_input():
return tf.constant([[[1, 1, 1], [2, 2, 2]],
[[3, 3, 3], [4, 4, 4]],
[[5, 5, 5], [6, 6, 6]]])
converter = Converter(tfe.get_config(), prot, 'model-provider')
x = converter.convert(graph_def, register(), 'input-provider',
provide_input)
with tfe.Session() as sess:
sess.run(prot.initializer, tag='init')
output = sess.run(x.reveal(), tag='reveal')
np.testing.assert_array_almost_equal(output, actual, decimal=3)
def test_mul_convert(self):
tf.reset_default_graph()
global global_filename
global_filename = "mul.pb"
input_shape = [4, 1]
path = export_mul(global_filename, input_shape)
tf.reset_default_graph()
graph_def = read_graph(path)
tf.reset_default_graph()
actual = run_mul(input_shape)
tf.reset_default_graph()
with tfe.protocol.Pond() as prot:
prot.clear_initializers()
def provide_input():
return tf.constant(
np.array([1.0, 2.0, 3.0, 4.0]).reshape(input_shape))
converter = Converter(tfe.get_config(), prot, 'model-provider')
x = converter.convert(graph_def, register(), 'input-provider',
provide_input)
with tfe.Session() as sess:
sess.run(prot.initializer, tag='init')
output = sess.run(x.reveal(), tag='reveal')
np.testing.assert_array_almost_equal(output, actual, decimal=3)
def test_cnn_NHWC_convert(self):
tf.reset_default_graph()
global global_filename
global_filename = "cnn_nhwc.pb"
input_shape = [1, 28, 28, 1]
path = export_cnn(global_filename, input_shape, data_format="NHWC")
tf.reset_default_graph()
graph_def = read_graph(path)
tf.reset_default_graph()
actual = run_cnn(input_shape, data_format="NHWC")
tf.reset_default_graph()
with tfe.protocol.Pond() as prot:
prot.clear_initializers()
def provide_input():
return tf.constant(np.ones(input_shape))
converter = Converter(tfe.get_config(), prot, 'model-provider')
x = converter.convert(graph_def, register(), 'input-provider',
provide_input)
with tfe.Session() as sess:
sess.run(prot.initializer, tag='init')
output = sess.run(x.reveal(), tag='reveal')
np.testing.assert_array_almost_equal(output, actual, decimal=3)
def test_argmax_convert(self):
tf.reset_default_graph()
global global_filename
global_filename = "argmax.pb"
input_shape = [5]
input = [1, 2, 3, 4, 5]
path = export_argmax(global_filename, input_shape, 0)
tf.reset_default_graph()
graph_def = read_graph(path)
tf.reset_default_graph()
actual = run_argmax(input, 0)
tf.reset_default_graph()
with tfe.protocol.SecureNN() as prot:
prot.clear_initializers()
def provide_input():
return tf.constant(np.ones(input_shape))
converter = Converter(tfe.get_config(), prot, 'model-provider')
x = converter.convert(graph_def, register(), 'input-provider', provide_input)
with tfe.Session() as sess:
sess.run(tf.global_variables_initializer())
output = sess.run(x.reveal(), tag='reveal')
np.testing.assert_array_almost_equal(output, actual, decimal=3)
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,
)
