torch.manual_seed(123456)
dataloaders, dataset_sizes = data_process_lisa(batch_size=128)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model_ft = Net()
model_ft.apply(weights_init)
#model_ft.load_state_dict(torch.load('../donemodel/'+args.model))
model_ft.to(device)
# model_ft = nn.DataParallel(model,device_ids=[0,1])
# use multiple gpus
criterion = nn.CrossEntropyLoss()
optimizer_ft = optim.Adam(model_ft.parameters(), lr=0.01)
# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft,
step_size=10,
gamma=0.1)
model_ft = pgd_train_model(model_ft,
criterion,
optimizer_ft,
exp_lr_scheduler,
num_epochs=30)
test(model_ft, dataloaders, dataset_sizes)
torch.save(model_ft.state_dict(),
'../donemodel/new_linf_model050.pt') # output model
model_ft.load_state_dict(torch.load('../donemodel/' + args.model))
#model_ft.load_weights()
model_ft.to(device)
# model_ft = nn.DataParallel(model,device_ids=[0,1])
criterion = nn.CrossEntropyLoss()
#optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)
optimizer_ft = optim.Adam(model_ft.parameters(), lr=0.01)
#optimizer_ft = optim.Adadelta(model_ft.parameters(), lr=0.1, rho=0.9, eps=1e-06, weight_decay=0.01)
# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft,
step_size=100,
gamma=0.3)
model_ft = sticker_train_model(model_ft,
criterion,
optimizer_ft,
exp_lr_scheduler,
args.alpha,
args.iters,
args.search,
num_epochs=args.epochs)
test(model_ft, dataloaders, dataset_sizes)
torch.save(
model_ft.state_dict(), '../donemodel/new_sticker_model0' +
str(args.out) + str(seed) + '.pt')