def test_cryptonets_relu(FLAGS):
(x_train, y_train, x_test, y_test) = load_mnist_data()
x = tf.compat.v1.placeholder(tf.float32, [None, 28, 28, 1], name='input')
y_ = tf.compat.v1.placeholder(tf.float32, [None, 10])
# Create the model
y_conv = cryptonets_relu_test_squashed(x)
config = server_config_from_flags(FLAGS, x.name)
print('config', config)
with tf.compat.v1.Session(config=config) as sess:
x_test = x_test[:FLAGS.batch_size]
y_test = y_test[:FLAGS.batch_size]
start_time = time.time()
y_conv_val = y_conv.eval(feed_dict={x: x_test, y_: y_test})
elasped_time = (time.time() - start_time)
print("total time(s)", np.round(elasped_time, 3))
if not FLAGS.enable_client:
y_test_batch = y_test[:FLAGS.batch_size]
y_label_batch = np.argmax(y_test_batch, 1)
y_pred = np.argmax(y_conv_val, 1)
correct_prediction = np.equal(y_pred, y_label_batch)
error_count = np.size(correct_prediction) - np.sum(correct_prediction)
test_accuracy = np.mean(correct_prediction)
print('Error count', error_count, 'of', FLAGS.batch_size, 'elements.')
print('Accuracy: %g ' % test_accuracy)
def test_network(FLAGS):
(x_train, y_train, x_test,
y_test) = load_mnist_data(FLAGS.start_batch, FLAGS.batch_size)
# Load saved model
tf.import_graph_def(load_pb_file(FLAGS.model_file))
print("loaded model")
print_nodes()
# Get input / output tensors
x_input = tf.compat.v1.get_default_graph().get_tensor_by_name(
FLAGS.input_node)
y_output = tf.compat.v1.get_default_graph().get_tensor_by_name(
FLAGS.output_node)
# Create configuration to encrypt input
FLAGS, unparsed = server_argument_parser().parse_known_args()
config = server_config_from_flags(FLAGS, x_input.name)
with tf.compat.v1.Session(config=config) as sess:
sess.run(tf.compat.v1.global_variables_initializer())
start_time = time.time()
y_hat = y_output.eval(feed_dict={x_input: x_test})
elasped_time = time.time() - start_time
print("total time(s)", np.round(elasped_time, 3))
if not FLAGS.enable_client:
y_test_label = np.argmax(y_test, 1)
if FLAGS.batch_size < 60:
print("y_hat", np.round(y_hat, 2))
y_pred = np.argmax(y_hat, 1)
correct_prediction = np.equal(y_pred, y_test_label)
error_count = np.size(correct_prediction) - np.sum(correct_prediction)
test_accuracy = np.mean(correct_prediction)
print("Error count", error_count, "of", FLAGS.batch_size, "elements.")
print("Accuracy: %g " % test_accuracy)
def test_mnist_mlp(FLAGS):
(x_train, y_train, x_test, y_test) = load_mnist_data()
x_test = x_test[:FLAGS.batch_size]
y_test = y_test[:FLAGS.batch_size]
graph_def = load_pb_file('./model/model.pb')
with tf.Graph().as_default():
tf.import_graph_def(graph_def)
y_conv = tf.compat.v1.get_default_graph().get_tensor_by_name(
"import/output:0")
x_input = tf.compat.v1.get_default_graph().get_tensor_by_name(
"import/input:0")
config = server_config_from_flags(FLAGS, x_input.name)
print('config', config)
with tf.compat.v1.Session(config=config) as sess:
start_time = time.time()
y_conv_val = y_conv.eval(
session=sess, feed_dict={
x_input: x_test,
})
elasped_time = (time.time() - start_time)
print("total time(s)", np.round(elasped_time, 3))
if not FLAGS.enable_client:
y_test_batch = y_test[:FLAGS.batch_size]
y_label_batch = np.argmax(y_test_batch, 1)
y_pred = np.argmax(y_conv_val, 1)
print('y_pred', y_pred)
correct_prediction = np.equal(y_pred, y_label_batch)
error_count = np.size(correct_prediction) - np.sum(correct_prediction)
test_accuracy = np.mean(correct_prediction)
print('Error count', error_count, 'of', FLAGS.batch_size, 'elements.')
print('Accuracy: %g ' % test_accuracy)
def run_server(FLAGS, query):
# tf.import_graph_def(load_pb_file(FLAGS.model_file))
tf.import_graph_def(
load_pb_file("/home/dockuser/models/cryptonets-relu.pb"))
# tf.import_graph_def(load_pb_file(f"/home/dockuser/models/{FLAGS.port}.pb"))
print("loaded model")
print_nodes()
print(f"query: {query.shape}")
# Get input / output tensors
x_input = tf.compat.v1.get_default_graph().get_tensor_by_name(
# FLAGS.input_node
# "import/Placeholder:0"
"import/input:0")
y_output = tf.compat.v1.get_default_graph().get_tensor_by_name(
"import/output/BiasAdd:0"
# FLAGS.output_node
# "import/dense/BiasAdd:0"
)
# weight_check = tf.compat.v1.get_default_graph().get_tensor_by_name("import/conv2d_1/kernel:0")
# print(weight_check)
# Load saved model
#
# model = models.Local_Model(FLAGS.num_classes, FLAGS.dataset_name) # load the model object
# input_x = tf.compat.v1.placeholder( # define input
# shape=(
# None,
# 3,
# 32,
# 32,
# ), name="input", dtype=tf.float32)
# init = tf.compat.v1.global_variables_initializer()
# model.build((None, 3, 32, 32))
# model.compile('adam', tf.keras.losses.CategoricalCrossentropy())
# output_y = model.get_out(input_x) # input through layers
# print("loaded model")
# print_nodes()
# print(input_x)
# print(output_y)
# Get input / output tensors
# x_input = tf.compat.v1.get_default_graph().get_tensor_by_name(
# FLAGS.input_node)
# y_output = tf.compat.v1.get_default_graph().get_tensor_by_name(
# FLAGS.output_node)
# y_max = tf.nn.softmax(y_output)
# y_max = y_output
# y_max = approximate_softmax(x=y_output, nr_terms=2)
# y_max = max_pool(y_output=y_output, FLAGS=FLAGS)
print('r_star: ', FLAGS.r_star)
r_star = np.array(FLAGS.r_star)
# r - r* (subtract the random vector r* from logits)
y_max = tf.subtract(y_output, tf.convert_to_tensor(r_star,
dtype=tf.float32))
# Create configuration to encrypt input
# config = server_config_from_flags(FLAGS, x_input.name)
config = server_config_from_flags(FLAGS, x_input.name)
with tf.compat.v1.Session(config=config) as sess:
sess.run(tf.compat.v1.global_variables_initializer())
# model.initialize_weights(FLAGS.model_file)
start_time = time.time()
print(f"query shape before processing: {query.shape}")
inference_start = time.time()
y_hat = sess.run(y_max, feed_dict={x_input: query})
inference_end = time.time()
print(f"Inference time: {inference_end - inference_start}s")
with open(inference_no_network_times_name, 'a') as outfile:
outfile.write(str(inference_end - inference_start))
outfile.write('\n')
elasped_time = time.time() - start_time
print("total time(s)", np.round(elasped_time, 3))
party_id = int(FLAGS.port)
msg = "doing 2pc"
print(msg)
log_timing(stage='server_client:' + msg,
log_file=FLAGS.log_timing_file)
print('r_star (r*): ', array_str(r_star))
r_star = round_array(x=r_star, exp=FLAGS.round_exp)
print('rounded r_star (r*): ', array_str(r_star))
if FLAGS.backend == 'HE_SEAL':
argmax_time_start = time.time()
with open(f'{out_server_name}{FLAGS.port}.txt',
'w') as outfile: # party id
# assume batch size of 1 for now TODO: make this work for > 1 batch size
for val in r_star.flatten():
outfile.write(f"{int(val)}" + '\n')
process = subprocess.Popen([
'./gc-emp-test/bin/argmax_1', '1', '12345',
f'{out_server_name}{FLAGS.port}.txt',
f'{out_final_name}{FLAGS.port}.txt'
])
# time.sleep(15)
process.wait()
argmax_time_end = time.time()
with open(argmax_times_name, 'a') as outfile:
outfile.write(str(argmax_time_end - argmax_time_start))
outfile.write("\n")
log_timing(stage='server_client:finished 2PC',
log_file=FLAGS.log_timing_file)
def test_mnist_cnn(FLAGS):
(x_train, y_train, x_test, y_test) = load_mnist_data()
x = tf.compat.v1.placeholder(tf.float32, [None, 28, 28, 1], name='input')
y_ = tf.compat.v1.placeholder(tf.float32, [None, 10])
# Create the model
#Using full dataset
y_conv = cryptonets_test_squashed(x)
config = server_config_from_flags(FLAGS, x.name)
print('config', config)
with tf.compat.v1.Session(config=config) as sess:
x_test = x_test[:FLAGS.batch_size]
y_test = y_test[:FLAGS.batch_size]
start_time = time.time()
y_conv_val = y_conv.eval(feed_dict={x: x_test, y_: y_test})
elasped_time = (time.time() - start_time)
print("total time(s)", np.round(elasped_time, 3))
print('y_conv_val', np.round(y_conv_val, 2))
y_test_batch = y_test[:FLAGS.batch_size]
y_label_batch = np.argmax(y_test_batch, 1)
correct_prediction = np.equal(np.argmax(y_conv_val, 1), y_label_batch)
error_count = np.size(correct_prediction) - np.sum(correct_prediction)
test_accuracy = np.mean(correct_prediction)
print('Using full dataset')
print('Error count:', error_count, 'of', FLAGS.batch_size, 'elements.')
print('Accuracy: ', test_accuracy)
#Using in HE_averaging
y_conv = cryptonets_HE_avg(x)
config = server_config_from_flags(FLAGS, x.name)
print('config', config)
with tf.compat.v1.Session(config=config) as sess:
x_test = x_test[:FLAGS.batch_size]
y_test = y_test[:FLAGS.batch_size]
start_time = time.time()
y_conv_val = y_conv.eval(feed_dict={x: x_test, y_: y_test})
elasped_time = (time.time() - start_time)
print("total time(s)", np.round(elasped_time, 3))
print('y_conv_val', np.round(y_conv_val, 2))
y_test_batch = y_test[:FLAGS.batch_size]
y_label_batch = np.argmax(y_test_batch, 1)
correct_prediction = np.equal(np.argmax(y_conv_val, 1), y_label_batch)
error_count = np.size(correct_prediction) - np.sum(correct_prediction)
test_accuracy = np.mean(correct_prediction)
print('Using HE_averaging')
print('Error count:', error_count, 'of', FLAGS.batch_size, 'elements.')
print('Accuracy: ', test_accuracy)
#Using in repeated sampling,mode 3
y_conv = cryptonets_test_squashed_mode(x,3)
config = server_config_from_flags(FLAGS, x.name)
print('config', config)
with tf.compat.v1.Session(config=config) as sess:
x_test = x_test[:FLAGS.batch_size]
y_test = y_test[:FLAGS.batch_size]
start_time = time.time()
y_conv_val = y_conv.eval(feed_dict={x: x_test, y_: y_test})
elasped_time = (time.time() - start_time)
print("total time(s)", np.round(elasped_time, 3))
print('y_conv_val', np.round(y_conv_val, 2))
y_test_batch = y_test[:FLAGS.batch_size]
y_label_batch = np.argmax(y_test_batch, 1)
correct_prediction = np.equal(np.argmax(y_conv_val, 1), y_label_batch)
error_count = np.size(correct_prediction) - np.sum(correct_prediction)
test_accuracy = np.mean(correct_prediction)
print('Using average of 4 partitions_repeated sampling')
print('Error count:', error_count, 'of', FLAGS.batch_size, 'elements.')
print('Accuracy: ', test_accuracy)
def run_server(FLAGS):
(x_train, y_train, x_test,
y_test) = load_mnist_data(FLAGS.start_batch, FLAGS.batch_size)
# Load saved model
tf.import_graph_def(load_pb_file(FLAGS.model_file))
print("loaded model")
print_nodes()
# Get input / output tensors
x_input = tf.compat.v1.get_default_graph().get_tensor_by_name(
FLAGS.input_node)
y_output = tf.compat.v1.get_default_graph().get_tensor_by_name(
FLAGS.output_node)
# y_max = tf.nn.softmax(y_output)
# y_max = y_output
# y_max = approximate_softmax(x=y_output, nr_terms=2)
# y_max = max_pool(y_output=y_output, FLAGS=FLAGS)
print('r_star: ', FLAGS.rstar)
print('is r_star [-1.0]', FLAGS.rstar == [-1.0])
r_star = None
if FLAGS.rstar is None:
# for debugging: we don't want any r*
y_max = y_output
else:
if FLAGS.rstar == [-1.0]:
# Generate random r_star
if y_test is not None:
r_shape = y_test.shape
batch_size = r_shape[0]
num_classes = r_shape[1]
else:
batch_size, num_classes = FLAGS.batch_size, FLAGS.num_classes
r_star = get_rstar_server(
max_logit=FLAGS.max_logit,
batch_size=batch_size,
num_classes=num_classes,
exp=FLAGS.rstar_exp,
)
else:
r_star = np.array(FLAGS.rstar)
# r - r* (subtract the random vector r* from logits)
y_max = tf.subtract(y_output,
tf.convert_to_tensor(r_star, dtype=tf.float32))
if FLAGS.debug is True:
print('y_max shape: ', y_max.shape)
print('r_star shape: ', r_star.shape)
y_max = tf.concat([y_max, r_star], axis=0)
# Create configuration to encrypt input
config = server_config_from_flags(FLAGS, x_input.name)
with tf.compat.v1.Session(config=config) as sess:
sess.run(tf.compat.v1.global_variables_initializer())
start_time = time.time()
y_hat = sess.run(y_max, feed_dict={x_input: x_test})
# y_hat = y_max.eval(feed_dict={x_input: x_test})
print('y_hat: ', y_hat)
if FLAGS.debug is True:
r_star = y_hat[FLAGS.batch_size:]
y_hat = y_hat[:FLAGS.batch_size]
print("logits (y_hat): ", array_str(y_hat))
print("logits (y_hat) type: ", type(y_hat))
print("logits (y_hat) shape: ", y_hat.shape)
# print("change y_hat to one_hot encoding")
y_pred = y_hat.argmax(axis=1)
print("y_pred: ", y_pred)
elasped_time = time.time() - start_time
print("total time(s)", np.round(elasped_time, 3))
party_id = int(FLAGS.port)
if r_star is not None:
print("doing 2pc")
print('r_star (r*): ', array_str(r_star))
if FLAGS.round_exp is not None:
# r_star = (r_star * 2 ** FLAGS.round_exp).astype(np.int64)
r_star = round_array(x=r_star, exp=FLAGS.round_exp)
print('rounded r_star (r*): ', array_str(r_star))
with open(f'{out_server_name}{party_id}.txt',
'w') as outfile: # party id
# assume batch size of 1 for now TODO: make this work for > 1 batch size
for val in r_star.flatten():
outfile.write(f"{int(val)}" + '\n')
time.sleep(1)
process = subprocess.Popen([
'./gc-emp-test/bin/argmax_1', '1', '12345',
f'{out_server_name}{party_id}.txt',
f'{out_final_name}{party_id}.txt'
])
# time.sleep(15)
process.wait()
else:
print('r_star is None in he_server.py!')
if not FLAGS.enable_client:
y_test_label = np.argmax(y_test, 1)
if FLAGS.batch_size < 60:
print("y_hat", np.round(y_hat, 2))
y_pred = np.argmax(y_hat, 1)
correct_prediction = np.equal(y_pred, y_test_label)
error_count = np.size(correct_prediction) - np.sum(correct_prediction)
test_accuracy = np.mean(correct_prediction)
print("Error count", error_count, "of", FLAGS.batch_size, "elements.")
print("Accuracy: %g " % test_accuracy)