DMD RuntimeError: 1D target tensor expects 2D input tensors, but found inputs with sizes [46, 16, 50] and [46, 16, 50].

How to solve this problem？

May 30 '23 13:05 JunLiangZ

How to solve this problem？

Hey, I've met the same problem. Did you solve it?

Aug 05 '23 09:08 MC-wither

How to solve this problem？

Hey, I've met the same problem. Did you solve it?

It seems the input dimensional dismatch of the call function of class nn.CosineEmbeddingLoss().

In the file trains/singleTask/DMD.py, replace the code

cosine_similarity_s_c_l = self.cosine(output['s_l'], output['c_l'], torch.tensor([-1]).cuda()).mean(0)
cosine_similarity_s_c_v = self.cosine(output['s_v'], output['c_v'], torch.tensor([-1]).cuda()).mean(0)
cosine_similarity_s_c_a = self.cosine(output['s_a'], output['c_a'], torch.tensor([-1]).cuda()).mean(0)

with

cosine_similarity_s_c_l = self.cosine(output['s_l'].reshape(-1, 50), output['c_l'].reshape(-1, 50), torch.tensor([-1]).cuda())
cosine_similarity_s_c_v = self.cosine(output['s_v'].reshape(-1, 50), output['c_v'].reshape(-1, 50), torch.tensor([-1]).cuda())
cosine_similarity_s_c_a = self.cosine(output['s_a'].reshape(-1, 50), output['c_a'].reshape(-1, 50), torch.tensor([-1]).cuda())

Aug 09 '23 06:08 MC-wither

How to solve this problem？

Hey, I've met the same problem. Did you solve it?

It seems the input dimensional dismatch of the call function of class nn.CosineEmbeddingLoss().

In the file trains/singleTask/DMD.py, replace the code

cosine_similarity_s_c_l = self.cosine(output['s_l'], output['c_l'], torch.tensor([-1]).cuda()).mean(0)
cosine_similarity_s_c_v = self.cosine(output['s_v'], output['c_v'], torch.tensor([-1]).cuda()).mean(0)
cosine_similarity_s_c_a = self.cosine(output['s_a'], output['c_a'], torch.tensor([-1]).cuda()).mean(0)

with

cosine_similarity_s_c_l = self.cosine(output['s_l'].reshape(-1, 50), output['c_l'].reshape(-1, 50), torch.tensor([-1]).cuda())
cosine_similarity_s_c_v = self.cosine(output['s_v'].reshape(-1, 50), output['c_v'].reshape(-1, 50), torch.tensor([-1]).cuda())
cosine_similarity_s_c_a = self.cosine(output['s_a'].reshape(-1, 50), output['c_a'].reshape(-1, 50), torch.tensor([-1]).cuda())

Hey, Is your solution to do cosine similarity on word Embeddings? If my code looks like this, is cosine similarity calculated at the sentence level?

                    cosine_similarity_s_c_l = self.cosine(output['s_l'].permute(1,0,2).contiguous().view(output['s_l'].size(1), -1),
                                                          output['c_l'].permute(1,0,2).contiguous().view(output['c_l'].size(1), -1),
                                                          torch.tensor([-1]).cuda()).mean(0)
                    cosine_similarity_s_c_v = self.cosine(output['s_v'].permute(1,0,2).contiguous().view(output['s_l'].size(1), -1),
                                                          output['c_v'].permute(1,0,2).contiguous().view(output['c_l'].size(1), -1),
                                                          torch.tensor([-1]).cuda()).mean(0)
                    cosine_similarity_s_c_a = self.cosine(output['s_a'].permute(1,0,2).contiguous().view(output['s_l'].size(1), -1),
                                                          output['c_a'].permute(1,0,2).contiguous().view(output['c_l'].size(1), -1),
                                                          torch.tensor([-1]).cuda()).mean(0)

Nov 23 '23 09:11 qiuqifeng2001

我是在文件trains/singleTask/DMD.py中，替换代码 cosine_similarity_s_c_l = self.cosine(output['s_l'], output['c_l'], torch.tensor([-1]).cuda()).mean(0) cosine_similarity_s_c_v = self.cosine(output['s_v'], output['c_v'], torch.tensor([-1]).cuda()).mean(0) cosine_similarity_s_c_a = self.cosine(output['s_a'], output['c_a'], torch.tensor([-1]).cuda()).mean(0) 成 cosine_similarity_s_c_l = self.cosine(output['s_l'].contiguous().view(labels.size(0),-1), output['c_l'].contiguous().view(labels.size(0),-1), torch.tensor([-1]).cuda()).mean(0) cosine_similarity_s_c_v = self.cosine(output['s_v'].contiguous().view(labels.size(0),-1), output['c_v'].contiguous().view(labels.size(0),-1), torch.tensor([-1]).cuda()).mean(0) cosine_similarity_s_c_a = self.cosine(output['s_a'].contiguous().view(labels.size(0),-1), output['c_a'].contiguous().view(labels.size(0),-1), torch.tensor([-1]).cuda()).mean(0)

Dec 17 '23 12:12 FAFUxrhuang

Hello, I encountered the same issue. I found the author's response in an old issue. It might be helpful to check the link below.

https://github.com/mdswyz/DMD/issues/5#issuecomment-1633787519

Sep 10 '24 11:09 JYeonKim

我是在文件trains/singleTask/DMD.py中，替换代码 cosine_similarity_s_c_l = self.cosine(output['s_l'], output['c_l'], torch.tensor([-1]).cuda()).mean(0) cosine_similarity_s_c_v = self.cosine(output['s_v'], output['c_v'], torch.tensor([-1]).cuda()).mean(0) cosine_similarity_s_c_a = self.cosine(output['s_a'], output['c_a'], torch.tensor([-1]).cuda()).mean(0) 成 cosine_similarity_s_c_l = self.cosine(output['s_l'].contiguous().view(labels.size(0),-1), output['c_l'].contiguous().view(labels.size(0),-1), torch.tensor([-1]).cuda()).mean(0) cosine_similarity_s_c_v = self.cosine(output['s_v'].contiguous().view(labels.size(0),-1), output['c_v'].contiguous().view(labels.size(0),-1), torch.tensor([-1]).cuda()).mean(0) cosine_similarity_s_c_a = self.cosine(output['s_a'].contiguous().view(labels.size(0),-1), output['c_a'].contiguous().view(labels.size(0),-1), torch.tensor([-1]).cuda()).mean(0)

谢谢了，有用解决了

Jan 06 '25 03:01 Issac304