args = parser.parse_args()
args.prefix = args.prefix or 'svhn_conv_{:.4f}_{:.4f}_0'.format(
args.epsilon, args.lr).replace(".", "_")
setproctitle.setproctitle(args.prefix)
train_log = open(args.prefix + "_train.log", "w")
test_log = open(args.prefix + "_test.log", "w")
train_loader, test_loader = pblm.svhn_loaders(args.batch_size)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
# new svhn
model = pblm.svhn_model().cuda()
if args.attack:
model.load_state_dict(torch.load('../models/svhn.pth'))
with open("attack_svhn.txt", "w") as f:
f.write("eps acc\n")
# for eps in range(1, 15):
for eps in range(1):
eps /= 10
# for eps in range(1, 17):
# eps *= 0.005
niters = 100
# alpha = 2.5 * eps / niters
alpha = 1e-2
# total_err, total_err_attack, _ = pgd_l2(test_loader, model,
total_err, total_err_attack, _ = carlini_l2(test_loader,
parser.add_argument('--mnist', action='store_true')
parser.add_argument('--svhn', action='store_true')
parser.add_argument('--har', action='store_true')
parser.add_argument('--fashion', action='store_true')
parser.add_argument('--model')
args = parser.parse_args()
if args.mnist:
train_loader, test_loader = pblm.mnist_loaders(args.batch_size)
model = pblm.mnist_model().to(device)
model.load_state_dict(torch.load('icml/mnist_epochs_100_baseline_model.pth'))
elif args.svhn:
train_loader, test_loader = pblm.svhn_loaders(args.batch_size)
model = pblm.svhn_model().to(device)
model.load_state_dict(torch.load('pixel2/svhn_small_batch_size_50_epochs_100_epsilon_0.0078_l1_proj_50_l1_test_median_l1_train_median_lr_0.001_opt_adam_schedule_length_20_seed_0_starting_epsilon_0.001_checkpoint.pth')['state_dict'])
elif args.model == 'cifar':
train_loader, test_loader = pblm.cifar_loaders(args.batch_size)
model = pblm.cifar_model().to(device)
model.load_state_dict(torch.load('pixel2/cifar_small_batch_size_50_epochs_100_epsilon_0.0347_l1_proj_50_l1_test_median_l1_train_median_lr_0.05_momentum_0.9_opt_sgd_schedule_length_20_seed_0_starting_epsilon_0.001_weight_decay_0.0005_checkpoint.pth')['state_dict'])
elif args.har:
pass
elif args.fashion:
pass
else:
raise ValueError("Need to specify which problem.")
for p in model.parameters():
p.requires_grad = False
num_classes = model[-1].out_features