print('Best val Acc: {:4f}'.format(best_acc))
# load best model weights
model.load_state_dict(best_model_wts)
return model
if __name__ == "__main__":
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,
return max_delta
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='test')
parser.add_argument("model", type=str, help="test_model")
args = parser.parse_args()
model = Net()
model.load_state_dict(torch.load('../donemodel/' + args.model))
print("test model is ", args.model)
model.eval()
batch_size = 1
dataloaders, dataset_sizes = data_process_lisa(batch_size)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)
eps = [0.5, 1, 1.5, 2, 2.5, 3] # eps is epsilon of the l_2 bound
alpha = [0.05, 0.1, 0.15, 0.2, 0.25, 0.3] # alpha is learning rate
itera = [20, 20, 20, 20, 20, 20] # iterations to find optimal
restart = [
1, 1, 1, 1, 1, 1
] # restart times, since we just do some standard check of our model,
# we do not use mutliple restarts, but you can change that if you want
# delete some hyperparmeters could speed up
for i in range(len(eps)):
correct = 0
total = 0
check = 0
torch.manual_seed(12345)