关于验证时维度不一致的问题

[CurryaNa]

你好，我在复现代码时出现了维度不一致的问题：

100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 181109/181109 [00:00<00:00, 389326.69it/s] Epoch: 0 Loss: 0.9171 Epoch: 100 Loss: 0.3691 Epoch: 200 Loss: 0.1924 Epoch: 300 Loss: 0.1232 Epoch: 400 Loss: 0.0847 Epoch: 500 Loss: 0.0723 Epoch: 600 Loss: 0.0584 Epoch: 700 Loss: 0.0560 Epoch: 800 Loss: 0.0451 Epoch: 900 Loss: 0.0368 Epoch: 1000 Loss: 0.0358 Epoch 1000 has finished, saving... Epoch 1000 has finished, validating... Traceback (most recent call last): File "/data/xuyiming/Few-shot/Edge-level/GANA-FewShotKGC/main_gana.py", line 63, in trainer.train() File "/data/xuyiming/Few-shot/Edge-level/GANA-FewShotKGC/trainer_gana.py", line 308, in train valid_data = self.eval(istest=False, epoch=e) File "/data/xuyiming/Few-shot/Edge-level/GANA-FewShotKGC/trainer_gana.py", line 363, in eval _, p_score, n_score = self.do_one_step(eval_task, iseval=True, curr_rel=curr_rel, istest=istest) File "/data/xuyiming/Few-shot/Edge-level/GANA-FewShotKGC/trainer_gana.py", line 281, in do_one_step loss = self.metaR.loss_func(p_score, n_score, y) File "/data/xuyiming/Conda/anaconda3/envs/enmark/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "/data/xuyiming/Conda/anaconda3/envs/enmark/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl return forward_call(*args, **kwargs) File "/data/xuyiming/Conda/anaconda3/envs/enmark/lib/python3.9/site-packages/torch/nn/modules/loss.py", line 1353, in forward return F.margin_ranking_loss(input1, input2, target, margin=self.margin, reduction=self.reduction) File "/data/xuyiming/Conda/anaconda3/envs/enmark/lib/python3.9/site-packages/torch/nn/functional.py", line 3416, in margin_ranking_loss raise RuntimeError( RuntimeError: margin_ranking_loss : All input tensors should have same dimension but got sizes: input1: torch.Size([1, 1]), input2: torch.Size([1, 2366]), target: torch.Size([1]) 应该是margin_ranking_loss的input1,input2和target维度应该是一致的，但是这里不一致，请问怎么解决呢