嵌入：参数索引必须是张量，而不是列表答案

【问题标题】：Embedding: argument indices must be a Tensor, not a list嵌入：参数索引必须是张量，而不是列表
【发布时间】：2021-07-25 12:06:34
【问题描述】：

我正在尝试训练 RNN，但我的嵌入有问题。我收到以下错误消息：

TypeError: embedding(): argument 'indices' (position 2) must be Tensor, not list

forward方法中的代码是这样开始的：

def forward(self, word_indices: [int]):
        print("sentences")
        print(len(word_indices))
        print(word_indices)

        word_ind_tensor = torch.tensor(word_indices, device="cpu")

        print(word_ind_tensor)
        print(word_ind_tensor.size())

        embeds_word = self.embedding_word(word_indices)

所有这些的输出是：

sentences
29
[261, 15, 5149, 44, 287, 688, 1125, 4147, 9874, 582, 15, 9875, 3, 2, 6732, 34, 2, 6733, 9, 2, 485, 7, 6734, 3, 741, 2, 2179, 1571, 1]
tensor([ 261,   15, 5149,   44,  287,  688, 1125, 4147, 9874,  582,   15, 9875,
           3,    2, 6732,   34,    2, 6733,    9,    2,  485,    7, 6734,    3,
         741,    2, 2179, 1571,    1])

torch.Size([29])

Traceback (most recent call last):
  File "/home/lukas/Documents/HU/Materialen/21SoSe-Studienprojekt/flair-Studienprojekt/TestModel.py", line 68, in <module>
    embeddings_storage_mode = "CPU") #auf cuda ändern
  File "/home/lukas/Documents/HU/Materialen/21SoSe-Studienprojekt/flair-Studienprojekt/flair/trainers/trainer.py", line 423, in train
    loss = self.model.forward_loss(batch_step)
  File "/home/lukas/Documents/HU/Materialen/21SoSe-Studienprojekt/flair-Studienprojekt/flair/models/sandbox/srl_tagger.py", line 122, in forward_loss
    features = self.forward(word_indices = sent_word_ind, frame_indices = sent_frame_ind)
  File "/home/lukas/Documents/HU/Materialen/21SoSe-Studienprojekt/flair-Studienprojekt/flair/models/sandbox/srl_tagger.py", line 147, in forward
    embeds_word = self.embedding_word(word_indices)
  File "/home/lukas/miniconda3/envs/studienprojekt/lib/python3.7/site-packages/torch/nn/modules/module.py", line 550, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/lukas/miniconda3/envs/studienprojekt/lib/python3.7/site-packages/torch/nn/modules/sparse.py", line 114, in forward
    self.norm_type, self.scale_grad_by_freq, self.sparse)
  File "/home/lukas/miniconda3/envs/studienprojekt/lib/python3.7/site-packages/torch/nn/functional.py", line 1724, in embedding
    return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
TypeError: embedding(): argument 'indices' (position 2) must be Tensor, not list

我最初通过以下方式初始化嵌入：

self.embedding_word = torch.nn.Embedding(self.word_dict_size, embedding_size)

word_dict_size 和 embedding_size 都是整数。是不是我做错了什么，还是更深层次的错误？

【问题讨论】：

标签： python-3.x pytorch embedding

【解决方案1】：

您将列表传递给self.embedding_word：word_indices，而不是您刚刚为此目的创建的张量word_ind_tensor。

【讨论】：

哦，哈哈。我没见过。很尴尬。谢谢！