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)