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

[JunLiangZ]

How to solve this problem？

[MC-wither]

How to solve this problem？

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

[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())

[ThousandLayerCake]

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)

[FAFUxrhuang]

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)