args.device = torch.device("cuda:" + str(args.gpu) if torch.cuda.is_available() else "cpu")
train_loader, test_loader, train_eval_loader = get_cifar10_loaders(data_aug=True, batch_size=args.tbsize)
model = cifar_model(args.model, layers=args.block, norm_type=args.norm, init_option=args.init)
logger.info(model)
if args.load != "none" :
model.load_state_dict(torch.load(os.path.join(args.load, "model_final.pt"), map_location=args.device)['state_dict'])
model.to(args.device)
loader = {"train_loader": train_loader, "train_eval_loader": train_eval_loader, "test_loader": test_loader}
if args.opt =="sgd" :
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, weight_decay=args.decay, momentum=0.9, nesterov=args.nesterov)
if args.adv == "none" :
if args.model == "ssp2" or args.model == "ssp3" :
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[70,120,160], gamma=0.1)
else :
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[60,100,140], gamma=0.1)
if args.epochs <= 100 :
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[30,60,90], gamma=0.1)
elif args.lr < 0.1 :
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[120,160,180], gamma=0.1)
else :
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[80,140,180], gamma=0.1)
elif args.opt == "adam" :
optimizer = torch.optim.Adam(model.parameters(), lr=0.0001, betas=(0., 0.9))
scheduler = None
adv_train = args.adv if args.adv != "none" else None
model = trainer(model, logger, loader, args, "cifar10", optimizer, scheduler, adv_train=adv_train)
model.to(args.device)
train_loader, test_loader, train_eval_loader = get_mnist_loaders()
loader = {
"train_loader": train_loader,
"train_eval_loader": train_eval_loader,
"test_loader": test_loader
}
if args.opt == "sgd":
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr)
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, milestones=[30, 60, 90], gamma=0.1)
elif args.opt == "adam":
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
betas=(0., 0.9))
scheduler = None
elif args.opt == "rms":
optimizer = torch.optim.RMSprop(model.parameters(), lr=1e-3)
scheduler = None
adv_train = args.adv if args.adv != "none" else None
model = trainer(model,
logger,
loader,
args,
"mnist",
optimizer,
scheduler,
adv_train=adv_train)