能否具体应用到这个代码中去: def forward(self, sentA, sentB, sent1_lengths, sent2_lengths): """ Performs the forward pass for each batch """ sentence_a_embed = self.pretrained_model.encode(sentA) sentence_b_embed = self.pretrained_model.encode(sentB) sentence_a_embed = torch.tensor(sentence_a_embed, dtype=torch.float) sentence_b_embed = torch.tensor(sentence_b_embed, dtype=torch.float) sentence_a_embed = sentence_a_embed.unsqueeze(1).repeat(1, sent1_lengths, 1) sentence_b_embed = sentence_b_embed.unsqueeze(1).repeat(1, sent2_lengths, 1)其中sentA是(batch_size, embedding_dim)数据,sen1_lengths是(squence_length)数据
时间: 2024-03-26 21:36:12 浏览: 31
可以将上述代码修改为以下代码:
```python
def forward(self, sentA, sentB, sent1_lengths, sent2_lengths):
"""
Performs the forward pass for each batch
"""
sentence_a_embed = self.pretrained_model.encode(sentA)
sentence_b_embed = self.pretrained_model.encode(sentB)
sentence_a_embed = torch.tensor(sentence_a_embed, dtype=torch.float)
sentence_b_embed = torch.tensor(sentence_b_embed, dtype=torch.float)
# 将sentence_a_embed在第1维度上扩展
sentence_a_embed = sentence_a_embed.unsqueeze(1).expand(-1, sent1_lengths, -1)
# 将sentence_b_embed在第1维度上扩展
sentence_b_embed = sentence_b_embed.unsqueeze(1).expand(-1, sent2_lengths, -1)
# 将sentence_a_embed和sent1_lengths拼接为(batch_size, sent1_lengths, embedding_dim + 1)的张量
sentence_a_embed = torch.cat([sentence_a_embed, sent1_lengths.unsqueeze(-1)], dim=2)
# 将sentence_b_embed和sent2_lengths拼接为(batch_size, sent2_lengths, embedding_dim + 1)的张量
sentence_b_embed = torch.cat([sentence_b_embed, sent2_lengths.unsqueeze(-1)], dim=2)
# 其他代码
...
```
其中,`-1`表示该维度的大小由其他维度的大小来推断。`unsqueeze(-1)`表示在最后一维度上增加1维,`unsqueeze(1)`表示在第1维度上增加1维。
这样可以将`sentA`和`sent1_lengths`拼接为(batch_size, sent1_lengths, embedding_dim + 1)的张量,将`sentB`和`sent2_lengths`拼接为(batch_size, sent2_lengths, embedding_dim + 1)的张量,用于后续的操作。
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