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()
config = tfe.LocalConfig([
'server0',
'server1',
'crypto_producer',
'prediction_client',
'weights_provider',
])
with tfe.protocol.Pond(*config.get_players(
'server0, server1, crypto_producer')) as prot:
prot.clear_initializers()
class PredictionClient(tfe.io.InputProvider):
self.input = None
def provide_input(self):
return tf.constant(self.input)
i1 = PredictionClient(config.get_player('prediction_client'))
i1.input = input1
i2 = PredictionClient(config.get_player('prediction_client'))
i2.input = input2
i3 = PredictionClient(config.get_player('prediction_client'))
i3.input = input3
input = [i1, i2, i3]
converter = Converter(config, prot,
config.get_player('weights_provider'))
x = converter.convert(graph_def, input, register())
with config.session() as sess:
tfe.run(sess, prot.initializer, tag='init')
output = x.reveal().eval(sess, 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()
config = tfe.LocalConfig([
'server0',
'server1',
'crypto_producer',
'prediction_client',
'weights_provider',
])
with tfe.protocol.Pond(*config.get_players(
'server0, server1, crypto_producer')) as prot:
prot.clear_initializers()
class PredictionClient(tfe.io.InputProvider):
def provide_input(self):
return tf.constant([[[1, 1, 1], [2, 2, 2]],
[[3, 3, 3], [4, 4, 4]],
[[5, 5, 5], [6, 6, 6]]])
input = PredictionClient(config.get_player('prediction_client'))
converter = Converter(config, prot,
config.get_player('weights_provider'))
x = converter.convert(graph_def, input, register())
with config.session() as sess:
tfe.run(sess, prot.initializer, tag='init')
output = x.reveal().eval(sess, tag='reveal')
np.testing.assert_array_almost_equal(output, actual, decimal=3)
def test_avgpooling_convert(self):
tf.reset_default_graph()
global global_filename
global_filename = "avgpool.pb"
input_shape = [1, 28, 28, 1]
path = export_avgpool(global_filename, input_shape)
tf.reset_default_graph()
graph_def = read_graph(path)
tf.reset_default_graph()
actual = run_avgpool(input_shape)
tf.reset_default_graph()
config = tfe.LocalConfig([
'server0',
'server1',
'crypto_producer',
'prediction_client',
'weights_provider',
])
with tfe.protocol.Pond(*config.get_players(
'server0, server1, crypto_producer')) as prot:
prot.clear_initializers()
class PredictionClient(tfe.io.InputProvider):
def provide_input(self):
return tf.constant(np.ones(input_shape))
input = PredictionClient(config.get_player('prediction_client'))
converter = Converter(config, prot,
config.get_player('weights_provider'))
x = converter.convert(graph_def, input, register())
with config.session() as sess:
tfe.run(sess, prot.initializer, tag='init')
output = x.reveal().eval(sess, tag='reveal')
np.testing.assert_array_almost_equal(output, actual, decimal=3)
export_cnn()
tf.reset_default_graph()
model_filename = 'cnn.pb'
with gfile.FastGFile(model_filename, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
config = tfe.LocalConfig([
'server0', 'server1', 'crypto_producer', 'prediction_client',
'weights_provider'
])
with tfe.protocol.Pond(
*config.get_players('server0, server1, crypto_producer')) as prot:
input = PredictionInputProvider(config.get_player('prediction_client'))
output = PredictionOutputReceiver(config.get_player('prediction_client'))
c = convert.Converter(config, prot, config.get_player('weights_provider'))
x = c.convert(graph_def, input, register())
prediction_op = prot.define_output(x, output)
with config.session() as sess:
tfe.run(sess, prot.initializer, tag='init')
tfe.run(sess, prediction_op, tag='prediction')