RuntimeError: Expected tensor for argument #1 'indices' to have one of the following scalar types: Long, Int; but got torch.FloatTensor instead (while checking arguments for embedding)

这个错误通常发生在使用PyTorch的嵌入层（embedding layer）时，参数中的索引类型不正确。嵌入层的索引应该是整数类型（Long或Int），但是你传入了一个浮点类型（FloatTensor）的张量。

为了解决这个问题，你可以将输入张量的类型转换为整数类型。你可以使用`.long()`方法或`.int()`方法将浮点类型的张量转换为长整数类型或整数类型。

例如，如果你的输入张量是`input_tensor`，你可以这样进行转换：

input_tensor = input_tensor.long()  # 或者 input_tensor = input_tensor.int()

然后，将转换后的张量传递给嵌入层进行处理。这样应该可以解决该错误。

相关问题

RuntimeError: Input type (torch.cuda.LongTensor) and weight type (torch.cuda.FloatTensor) should be the same

This error occurs when you are trying to perform an operation on a tensor and its corresponding weight, but their data types do not match. In this specific case, you are trying to perform an operation on a tensor of type `torch.cuda.LongTensor` and its corresponding weight of type `torch.cuda.FloatTensor`.

To resolve this error, you need to ensure that the data types of the tensor and its weight are the same. You can do this by converting one of them to match the other.

For example, if you want to convert the weight to type `torch.cuda.LongTensor`, you can use the `.long()` method to convert it:

weight = weight.long()

Alternatively, if you want to convert the tensor to type `torch.cuda.FloatTensor`, you can use the `.float()` method to convert it:

tensor = tensor.float()

Once you have converted one of the variables to match the other, you should be able to perform the operation without any issues.