def prepare_eval_real_data(self):
        """Prepare evaluation real data, contains k_label batches of data"""
        with torch.no_grad():
            self.eval_real_samples = [torch.cat(
                [F.one_hot(self.oracle_data_list[i].random_batch()['target'], cfg.vocab_size).float()
                 for _ in range(cfg.eval_b_num)], dim=0) for i in range(cfg.k_label)]
            if cfg.CUDA:
                self.eval_real_samples = [self.eval_real_samples[i].cuda() for i in range(cfg.k_label)]

            if cfg.eval_type == 'rsgan' or cfg.eval_type == 'Ra':
                self.eval_d_out_real = [self.dis(self.eval_real_samples[i]) for i in range(cfg.k_label)]
Prepare evaluation real data, contains k_label batches of data
instructor/oracle_data/catgan_instructor.py