_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
loss = criterion(outputs, labels)
loss_sum += loss.data.cpu().item() * images.size(0)
correct += (predicted == labels).sum().item()
cnt += int(images.size()[0])
print('Accuracy of the network on the 10000 test images: %f %%' %
(100 * correct / total))
print("loss=", loss_sum / float(cnt))
elapsed_time = time.time() - inference_start
print("Elapsed time for Prediction", elapsed_time)
if __name__ == "__main__":
_, _, _, test_to_run = argparser_distributed()
print("====== New Tests ======")
print("Test To run:", test_to_run)
net_state_name, config_name = get_net_config_name(test_to_run)
print(f"net_state going to load: {net_state_name}")
print(f"store_configs going to load: {config_name}")
store_configs = np.load(config_name, allow_pickle="TRUE").item()
def input_shift(data):
first_layer_name = "conv1"
return data_shift(data, store_configs[first_layer_name + "ForwardX"])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
p = Process(target=run_secure_nn_client_with_random_data,
args=[secure_nn, correctness_func, master_address, master_port])
p.start()
processes.append(p)
for p in processes:
p.join()
if party == Config.server_rank:
run_secure_nn_server_with_random_data(secure_nn, correctness_func, master_address, master_port)
if party == Config.client_rank:
run_secure_nn_client_with_random_data(secure_nn, correctness_func, master_address, master_port)
print(f"\nTest for {test_name}: End")
if __name__ == "__main__":
input_sid, master_addr, master_port, test_to_run = argparser_distributed()
sys.stdout = Logger()
print("====== New Tests ======")
print("Test To run:", test_to_run)
num_repeat = 5
for _ in range(num_repeat):
if test_to_run in ["small", "all"]:
marshal_secure_nn_parties(input_sid, master_addr, master_port, generate_small_nn(), correctness_small_nn)
if test_to_run in ["relu", "all"]:
marshal_secure_nn_parties(input_sid, master_addr, master_port, generate_relu_only_nn(), correctness_relu_only_nn)
if test_to_run in ["maxpool", "all"]:
marshal_secure_nn_parties(input_sid, master_addr, master_port, generate_maxpool2x2(), correctness_maxpool2x2)
if test_to_run in ["conv2d", "all"]: