seed=args.seed,
nway=args.nway)
trainer_dict = {'args': args, 'logger': logger}
trainer = Trainer(trainer_dict)
test_data_list, test_label_list = te_dataloader.get_few_data_list()
test_data_array, test_label_array = np.stack(
test_data_list), np.hstack(test_label_list)
if args.load:
model_path = os.path.join(args.load_dir, 'model.pth')
trainer.load_model(model_path)
test_pred = trainer.test(test_data_array, te_dataloader)
print(test_pred.shape, test_label_array.shape)
correct = (test_pred == test_label_array).sum()
test_acc = (test_pred == test_label_array).mean() * 100.0
print('test_acc: %.4f %%, correct: %d / %d' %
(test_acc, correct, len(test_label_array)))
if __name__ == '__main__':
args = parser()
os.environ['CUDA_VISIBLE_DEVICES'] = args.use_gpu
main(args)
save=args.delta_path + args.save)
return
elif args.todo == "class_wise":
# Generate class wise error minimizing noise
perturbation.class_wise(train_loader,
num_classes=10,
save=args.delta_path + args.save)
return
else:
raise NotImplementedError
if __name__ == "__main__":
args = argument.parser()
argument.print_args(args)
# Set seed and device
seed = 0
if torch.cuda.is_available() and args.gpu != 0:
device = torch.device("cuda")
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
else:
device = torch.device("cpu")
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
