def prepare_eval_fake_data(self):
        """Prepare evaluation fake data, contains k_label batches of data"""
        with torch.no_grad():
            self.eval_fake_samples = [self.gen.sample(cfg.eval_b_num * cfg.batch_size,
                                                      cfg.eval_b_num * cfg.batch_size, one_hot=True, label_i=i)
                                      for i in range(cfg.k_label)]
            if cfg.CUDA:
                self.eval_fake_samples = [self.eval_fake_samples[i].cuda() for i in range(cfg.k_label)]

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