コード例 #1
0
def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('--resume',
                        '--resume',
                        default='log/models/epoch-104.checkpoint',
                        type=str,
                        metavar='PATH')
    parser.add_argument('-d', type=int, default=0, help='Which gpu to use')
    args = parser.parse_args()

    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    torch.backends.cudnn.benchmark = True

    net = create_network()
    if torch.cuda.device_count() > 1:
        print('Let\'s use {} GPUs!'.format(torch.cuda.device_count()))
        net = nn.DataParallel(net)

    net.to(device)

    ds_val = create_test_dataset()

    attack_method = config.create_evaluation_attack_method(device)

    if os.path.isfile(args.resume):
        load_checkpoint(args.resume, net)

    print('Evaluating')
    clean_acc, adv_acc = eval_one_epoch(net, ds_val, device, attack_method)
    print('clean acc: {}, adv acc: {}'.format(clean_acc, adv_acc))
    file1 = open(os.path.join(config.model_dir, 'mi_eval_results.txt'), 'w')
    file1.write('MI Eval, clean acc: {}, adv acc: {}'.format(
        clean_acc, adv_acc))
    file1.close()
コード例 #2
0
def main():
    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    torch.backends.cudnn.benchmark = True

    net = create_network()
    net.to(device)
    criterion = config.create_loss_function().to(device)

    optimizer = config.create_optimizer(net.parameters())
    lr_scheduler = config.create_lr_scheduler(optimizer)

    ds_train = create_train_dataset(args.batch_size)
    ds_val = create_test_dataset(args.batch_size)

    train_attack = config.create_attack_method(device)
    eval_attack = config.create_evaluation_attack_method(device)

    now_epoch = 0

    if args.auto_continue:
        args.resume = os.path.join(config.model_dir, 'last.checkpoint')
    if args.resume is not None and os.path.isfile(args.resume):
        now_epoch = load_checkpoint(args.resume, net, optimizer, lr_scheduler)

    for i in range(now_epoch, config.num_epochs):

        descr_str = 'Epoch:{}/{} -- lr:{}'.format(
            i, config.num_epochs,
            lr_scheduler.get_last_lr()[0])
        cleanacc, advacc = train_one_epoch(net,
                                           ds_train,
                                           optimizer,
                                           criterion,
                                           device,
                                           descr_str,
                                           train_attack,
                                           adv_coef=args.adv_coef)
        tb_train_dic = {'Acc': cleanacc, 'YofoAcc': advacc}
        print('Train: {}'.format(tb_train_dic))

        if config.eval_interval > 0 and i % config.eval_interval == 0:
            acc, advacc = eval_one_epoch(net, ds_val, device, eval_attack)
            tb_val_dic = {'Acc': acc, 'AdvAcc': advacc}
            print('Eval: {}'.format(tb_val_dic))

        lr_scheduler.step()

    save_checkpoint(i,
                    net,
                    optimizer,
                    lr_scheduler,
                    file_name=os.path.join(config.model_dir,
                                           'advtrain.{}'.format(i)))
    acc, advacc = eval_one_epoch(net, ds_val, device, eval_attack)
    tb_val_dic = {'Acc': acc, 'AdvAcc': advacc}
    print('Eval: {}'.format(tb_val_dic))
コード例 #3
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def main():
    device = 'cuda' if torch.cuda.is_available() else 'cpu'

    net = create_network()
    net.to(device)

    criterion = config.create_loss_function().to(device)
    optimizer = config.create_optimizer(net.other_layers.parameters())
    lr_scheduler = config.create_lr_scheduler(optimizer)

    Hamiltonian_func = Hamiltonian(net.layer_one, config.weight_decay)
    layer_one_optimizer = optim.SGD(net.layer_one.parameters(), lr=lr_scheduler.get_last_lr()[0], momentum=0.9,
                                    weight_decay=5e-4)
    lyaer_one_optimizer_lr_scheduler = optim.lr_scheduler.MultiStepLR(layer_one_optimizer,
                                                                      milestones=[70, 90, 100], gamma=0.1)
    LayerOneTrainer = FastGradientLayerOneTrainer(Hamiltonian_func, layer_one_optimizer,
                                                  config.inner_iters, config.sigma, config.eps)

    ds_train = create_train_dataset(args.batch_size)
    ds_eval = create_test_dataset(args.batch_size)

    eval_attack = config.create_evaluation_attack_method(device)

    now_epoch = 0
    if args.auto_continue:
        args.resume = os.path.join(config.model_dir, 'last.checkpoint')
    if args.resume is not None and os.path.isfile(args.resume):
        now_epoch = load_checkpoint(args.resume, net, optimizer, lr_scheduler)

    for i in range(now_epoch, config.num_epochs):

        descrip_str = 'Training epoch:{}/{} -- lr:{}'.format(i, config.num_epochs,
                                                             lr_scheduler.get_last_lr()[0])
        acc, yofoacc = train_one_epoch(net, ds_train, optimizer, criterion, LayerOneTrainer, config.K,
                                       device, descrip_str)
        tb_train_dic = {'Acc': acc, 'YofoAcc': yofoacc}
        print('Train: {}'.format(tb_train_dic))

        if config.eval_interval > 0 and i % config.eval_interval == 0:
            acc, advacc = eval_one_epoch(net, ds_eval, device, eval_attack)
            tb_val_dic = {'Acc': acc, 'AdvAcc': advacc}
            print('Eval: {}'.format(tb_val_dic))

        lr_scheduler.step()
        lyaer_one_optimizer_lr_scheduler.step()

    save_checkpoint(i, net, optimizer, lr_scheduler,
                    file_name=os.path.join(config.model_dir, 'epoch-{}.checkpoint'.format(i)))

    acc, advacc = eval_one_epoch(net, ds_eval, device, eval_attack)
    tb_val_dic = {'Acc': acc, 'AdvAcc': advacc}
    print('Eval: {}'.format(tb_val_dic))
コード例 #4
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def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('--resume', '--resume', default='log/models/last.checkpoint',
                        type=str, metavar='PATH',
                        help='path to latest checkpoint (default:log/last.checkpoint)')
    parser.add_argument('-d', type=int, default=0, help='Which gpu to use')
    args = parser.parse_args()

    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    torch.backends.cudnn.benchmark = True

    net = create_network()
    net.to(device)

    ds_eval = create_test_dataset(100)

    attack_method = config.create_evaluation_attack_method(device)

    if os.path.isfile(args.resume):
        load_checkpoint(args.resume, net)

    print('Evaluating')
    clean_acc, adv_acc = eval_one_epoch(net, ds_eval, device, attack_method)
    print('clean acc: {}; adv acc: {}'.format(clean_acc, adv_acc))
コード例 #5
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LayerOneTrainer = FastGradientLayerOneTrainer(Hamiltonian_func,
                                              layer_one_optimizer,
                                              config.inner_iters, config.sigma,
                                              config.eps)

ds_train = create_train_dataset(args.batch_size)
ds_val = create_test_dataset(args.batch_size)

EvalAttack = config.create_evaluation_attack_method(DEVICE)

now_epoch = 0

if args.auto_continue:
    args.resume = os.path.join(config.model_dir, 'last.checkpoint')
if args.resume is not None and os.path.isfile(args.resume):
    now_epoch = load_checkpoint(args.resume, net, optimizer, lr_scheduler)

start_time = datetime.datetime.now()
log_path = "epoch-vs-time.txt"
log_file = open(log_path, 'w')
log_file.write("{} {}\n".format(now_epoch, 0))
now_train_time = 0
while True:
    if now_epoch > config.num_epochs:
        break
    now_epoch = now_epoch + 1

    descrip_str = 'Training epoch:{}/{} -- lr:{}'.format(
        now_epoch, config.num_epochs,
        lr_scheduler.get_lr()[0])
    s_time = time.time()
コード例 #6
0
def main():
    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    torch.backends.cudnn.benchmark = True

    # writer = SummaryWriter(log_dir=config.log_dir)

    net = create_network()
    net.to(device)
    criterion = config.create_loss_function().to(device)
    # criterion = CrossEntropyWithWeightPenalty(net.other_layers, DEVICE, config.weight_decay)#.to(DEVICE)
    # ce_criterion = nn.CrossEntropyLoss().to(DEVICE)
    optimizer = config.create_optimizer(net.other_layers.parameters())
    lr_scheduler = config.create_lr_scheduler(optimizer)

    # Make Layer One trainer  This part of code should be writen in config.py

    Hamiltonian_func = Hamiltonian(net.layer_one, config.weight_decay)
    layer_one_optimizer = optim.SGD(net.layer_one.parameters(),
                                    lr=lr_scheduler.get_last_lr()[0],
                                    momentum=0.9,
                                    weight_decay=5e-4)
    layer_one_optimizer_lr_scheduler = optim.lr_scheduler.MultiStepLR(
        layer_one_optimizer, milestones=[55, 75, 95], gamma=0.1)
    LayerOneTrainer = FastGradientLayerOneTrainer(Hamiltonian_func,
                                                  layer_one_optimizer,
                                                  config.inner_iters,
                                                  config.sigma, config.eps)

    ds_train = create_train_dataset(args.batch_size)
    ds_val = create_test_dataset(args.batch_size)

    # TrainAttack = config.create_attack_method(DEVICE)
    eval_attack = config.create_evaluation_attack_method(device)

    now_epoch = 0

    if args.auto_continue:
        args.resume = os.path.join(config.model_dir, 'last.checkpoint')
    if args.resume is not None and os.path.isfile(args.resume):
        now_epoch = load_checkpoint(args.resume, net, optimizer, lr_scheduler)

    for i in range(now_epoch, config.num_epochs):

        descrip_str = 'Epoch:{}/{} -- lr:{}'.format(
            i, config.num_epochs,
            lr_scheduler.get_last_lr()[0])
        acc, yofoacc = train_one_epoch(net, ds_train, optimizer, criterion,
                                       LayerOneTrainer, config.K, device,
                                       descrip_str)
        tb_train_dic = {'Acc': acc, 'YofoAcc': yofoacc}
        print('Train: {}'.format(tb_train_dic))

        if config.eval_interval > 0 and i % config.eval_interval == 0:
            acc, advacc = eval_one_epoch(net, ds_val, device, eval_attack)
            tb_val_dic = {'Acc': acc, 'AdvAcc': advacc}
            print('Eval: {}'.format(tb_val_dic))

        lr_scheduler.step()
        layer_one_optimizer_lr_scheduler.step()

    save_checkpoint(i,
                    net,
                    optimizer,
                    lr_scheduler,
                    file_name=os.path.join(config.model_dir,
                                           'epoch-{}.checkpoint'.format(i)))
    acc, advacc = eval_one_epoch(net, ds_val, device, eval_attack)
    tb_val_dic = {'Acc': acc, 'AdvAcc': advacc}
    print('Eval: {}'.format(tb_val_dic))