def main(FLAGS):
if FLAGS.enable_client:
print("Using client")
else:
print("Not using client")
imagenet_inference_labels = get_imagenet_inference_labels()
imagenet_training_labels = get_imagenet_training_labels()
util.VAL_IMAGE_FLAGS = FLAGS
assert sorted(imagenet_training_labels) == sorted(
imagenet_inference_labels)
validation_nums = get_validation_labels(FLAGS)
validation_labels = imagenet_inference_labels[validation_nums]
if FLAGS.enable_client:
# Server input is dummy
x_test = np.random.rand(FLAGS.batch_size, FLAGS.image_size,
FLAGS.image_size, 3)
else:
x_test = get_validation_images(FLAGS)
config = server_config_from_flags(FLAGS, "input")
sess = tf.compat.v1.Session(config=config)
graph_def = load_model(FLAGS.model)
tf.import_graph_def(graph_def, name="")
input_tensor = sess.graph.get_tensor_by_name("input:0")
output_tensor = sess.graph.get_tensor_by_name(
"MobilenetV2/Logits/Conv2d_1c_1x1/BiasAdd:0")
print("performing inference")
start_time = time.time()
y_pred = sess.run(output_tensor, {input_tensor: x_test})
end_time = time.time()
runtime = end_time - start_time
per_image_runtime = runtime / float(FLAGS.batch_size)
print("performed inference, runtime (s):", np.round(runtime, 2))
print("runtime per image (s)", np.round(per_image_runtime, 2))
y_pred = np.squeeze(y_pred)
if FLAGS.batch_size == 1:
top5 = y_pred.argsort()[-5:]
else:
top5 = np.flip(y_pred.argsort()[:, -5:], axis=1)
if not FLAGS.enable_client:
preds = imagenet_training_labels[top5]
if FLAGS.batch_size < 10:
print("validation_labels", validation_labels)
print("validation_labels shape", validation_labels.shape)
print("preds", preds)
print("preds shape", preds.shape)
util.accuracy(preds, validation_labels)
def main(FLAGS):
util.VAL_IMAGE_FLAGS = FLAGS
imagenet_inference_labels = get_imagenet_inference_labels()
imagenet_training_labels = get_imagenet_training_labels()
assert (
sorted(imagenet_training_labels) == sorted(imagenet_inference_labels))
validation_nums = get_validation_labels(FLAGS)
x_test = get_validation_images(FLAGS)
validation_labels = imagenet_inference_labels[validation_nums]
if FLAGS.batch_size < 10:
print('validation_labels', validation_labels)
(batch_size, width, height, channels) = x_test.shape
print('batch_size', batch_size)
print('width', width)
print('height', height)
print('channels', channels)
x_test_flat = x_test.flatten(order='C')
hostname = 'localhost'
port = 34000
if 'NGRAPH_COMPLEX_PACK' in os.environ:
complex_packing = str2bool(os.environ['NGRAPH_COMPLEX_PACK'])
else:
complex_packing = False
client = pyhe_client.HESealClient(FLAGS.hostname, port, batch_size,
x_test_flat, complex_packing)
while not client.is_done():
time.sleep(1)
results = client.get_results()
imagenet_labels = get_imagenet_labels()
results = np.array(results)
if (FLAGS.batch_size == 1):
top5 = results.argsort()[-5:]
else:
results = np.reshape(results, (FLAGS.batch_size, 1001))
top5 = np.flip(results.argsort()[:, -5:], axis=1)
preds = imagenet_labels[top5]
print('validation_labels', validation_labels)
print('top5', preds)
util.accuracy(preds, validation_labels)
def main(FLAGS):
imagenet_inference_labels = get_imagenet_inference_labels()
imagenet_training_labels = get_imagenet_training_labels()
assert (
sorted(imagenet_training_labels) == sorted(imagenet_inference_labels))
validation_nums = get_validation_labels(FLAGS)
x_test = get_validation_images(FLAGS)
validation_labels = imagenet_inference_labels[validation_nums]
if FLAGS.batch_size < 10:
print('validation_labels', validation_labels)
(batch_size, width, height, channels) = x_test.shape
print('batch_size', batch_size)
print('width', width)
print('height', height)
print('channels', channels)
x_test_flat = x_test.flatten(order='C')
port = 34000
client = pyhe_client.HESealClient(FLAGS.hostname, port, batch_size,
{'input': ('encrypt', x_test_flat)})
results = client.get_results()
imagenet_labels = get_imagenet_labels()
results = np.array(results)
if (FLAGS.batch_size == 1):
top5 = results.argsort()[-5:]
else:
results = np.reshape(results, (FLAGS.batch_size, 1001))
top5 = np.flip(results.argsort()[:, -5:], axis=1)
preds = imagenet_labels[top5]
print('validation_labels', validation_labels)
print('top5', preds)
util.accuracy(preds, validation_labels)
def main(FLAGS):
if FLAGS.enable_client:
print('Using client')
else:
print('Not using client')
imagenet_inference_labels = get_imagenet_inference_labels()
imagenet_training_labels = get_imagenet_training_labels()
util.VAL_IMAGE_FLAGS = FLAGS
assert (
sorted(imagenet_training_labels) == sorted(imagenet_inference_labels))
validation_nums = get_validation_labels(FLAGS)
validation_labels = imagenet_inference_labels[validation_nums]
if FLAGS.enable_client:
# Server input is dummy
x_test = np.random.rand(FLAGS.batch_size, FLAGS.image_size,
FLAGS.image_size, 3)
else:
x_test = get_validation_images(FLAGS)
config = server_config_from_flags(FLAGS, 'input')
sess = tf.compat.v1.Session(config=config)
graph_def = load_model(FLAGS.model)
for node in graph_def.node:
if 'FusedBatchNorm' in node.name or 'Pow' in node.name:
print(node)
#print('node names', [n.name for n in graph_def.node])
tf.import_graph_def(graph_def, name='')
input_tensor = sess.graph.get_tensor_by_name('input:0')
output_tensor = sess.graph.get_tensor_by_name(
'MobilenetV2/Logits/Conv2d_1c_1x1/BiasAdd:0')
print('performing inference')
start_time = time.time()
y_pred = sess.run(output_tensor, {input_tensor: x_test})
end_time = time.time()
runtime = end_time - start_time
per_image_runtime = runtime / float(FLAGS.batch_size)
print('performed inference, runtime (s):', np.round(runtime, 2))
print('runtime per image (s)', np.round(per_image_runtime, 2))
y_pred = np.squeeze(y_pred)
if (FLAGS.batch_size == 1):
top5 = y_pred.argsort()[-5:]
else:
top5 = np.flip(y_pred.argsort()[:, -5:], axis=1)
if not FLAGS.enable_client:
preds = imagenet_training_labels[top5]
if FLAGS.batch_size < 10:
print('validation_labels', validation_labels)
print('validation_labels shape', validation_labels.shape)
print('preds', preds)
print('preds shape', preds.shape)
util.accuracy(preds, validation_labels)
def main(FLAGS):
using_client = (os.environ.get('NGRAPH_ENABLE_CLIENT') is not None)
if using_client:
print('Using client')
else:
print('Not using client')
imagenet_inference_labels = get_imagenet_inference_labels()
imagenet_training_labels = get_imagenet_training_labels()
util.VAL_IMAGE_FLAGS = FLAGS
assert (
sorted(imagenet_training_labels) == sorted(imagenet_inference_labels))
if not using_client:
validation_nums = get_validation_labels(FLAGS)
x_test = get_validation_images(FLAGS)
validation_labels = imagenet_inference_labels[validation_nums]
else:
x_test = np.random.rand(FLAGS.batch_size, FLAGS.image_size,
FLAGS.image_size, 3)
if FLAGS.ngraph:
import ngraph_bridge
print(ngraph_bridge.__version__)
config = tf.compat.v1.ConfigProto()
config.intra_op_parallelism_threads = 44
config.inter_op_parallelism_threads = 44
if FLAGS.ngraph:
config = ngraph_bridge.update_config(config)
sess = tf.compat.v1.Session(config=config)
graph_def = load_model(FLAGS.model)
tf.import_graph_def(graph_def, name='')
input_tensor = sess.graph.get_tensor_by_name('input:0')
output_tensor = sess.graph.get_tensor_by_name(
'MobilenetV2/Logits/Conv2d_1c_1x1/BiasAdd:0')
print('performing inference')
start_time = time.time()
y_pred = sess.run(output_tensor, {input_tensor: x_test})
end_time = time.time()
runtime = end_time - start_time
per_image_runtime = runtime / float(FLAGS.batch_size)
print('performed inference, runtime (s):', np.round(runtime, 2))
print('runtime per image (s)', np.round(per_image_runtime, 2))
y_pred = np.squeeze(y_pred)
if (FLAGS.batch_size == 1):
top5 = y_pred.argsort()[-5:]
else:
top5 = np.flip(y_pred.argsort()[:, -5:], axis=1)
if not using_client:
preds = imagenet_training_labels[top5]
if FLAGS.batch_size < 10:
print('validation_labels', validation_labels)
print('validation_labels shape', validation_labels.shape)
print('preds', preds)
print('preds shape', preds.shape)
util.accuracy(preds, validation_labels)
def main(FLAGS):
util.VAL_IMAGE_FLAGS = FLAGS
imagenet_inference_labels = get_imagenet_inference_labels()
imagenet_training_labels = get_imagenet_training_labels()
assert (
sorted(imagenet_training_labels) == sorted(imagenet_inference_labels))
validation_nums = get_validation_labels(FLAGS)
x_test = get_validation_images(FLAGS)
validation_labels = imagenet_inference_labels[validation_nums]
if FLAGS.batch_size < 10:
print('validation_labels', validation_labels)
(batch_size, width, height, channels) = x_test.shape
print('batch_size', batch_size)
print('width', width)
print('height', height)
print('channels', channels)
# Reshape to expected format (batch axes innermost)
x_test = np.moveaxis(x_test, 0, -1)
x_test_flat = x_test.flatten(order='C"')
hostname = 'localhost'
port = 34000
complex_scale_factor = 1
if ('NGRAPH_COMPLEX_PACK' in os.environ):
complex_scale_factor = 2
print('complex_scale_factor', complex_scale_factor)
# TODO: support even batch sizes
assert (batch_size % complex_scale_factor == 0)
new_batch_size = batch_size // complex_scale_factor
client = he_seal_client.HESealClient(FLAGS.hostname, port, new_batch_size,
x_test_flat)
while not client.is_done():
time.sleep(1)
results = client.get_results()
imagenet_labels = get_imagenet_labels()
results = np.array(results)
if (FLAGS.batch_size == 1):
top5 = results.argsort()[-5:]
else:
print('results shape', results.shape)
results = np.reshape(results, (
1001,
FLAGS.batch_size,
))
print('results.shape', results.shape)
try:
res_sort = results.argsort(axis=0)
res_top5 = res_sort[-5:, :]
top5x = np.flip(res_top5, axis=0)
top5 = top5x
top5 = top5.T
print('top5.shape', top5.shape)
except e:
print('e', e)
preds = imagenet_labels[top5]
print('validation_labels', validation_labels)
print('top5', preds)
util.accuracy(preds, validation_labels)
