Sizes of tensors must match except in dimension 1. Expected size 18 but got size 16 for tensor number 1 in the list

这个错误通常发生在 PyTorch 中使用了不匹配的张量。在这种情况下，模型期望的张量维度与实际张量维度不匹配，除非在维度1上。

要解决这个问题，需要检查代码中所有涉及张量的操作，确保它们的维度是一致的。还可以使用`tensor.size()`函数检查张量的形状，以确保它们具有正确的维度。

如果您使用的是 DataLoader，请确保您的数据集中所有的样本都具有相同的张量形状。如果您在训练期间使用了不同形状的张量，则可以使用 PyTorch 的`pad_sequence`函数将它们填充到相同的长度，以便它们能够在一个批次中一起处理。

相关问题

Sizes of tensors must match except in dimension 1. Expected size 8 but got size 16 for tensor number 1 in the list.

这个错误通常出现在使用 PyTorch 的 nn.ModuleList 或 nn.Sequential 时，输入的 tensor 维度不匹配。在你的情况下，该错误表示输入到模型的 tensor 在指定维度上的大小不一致。请检查你的输入数据和模型的输入维度是否匹配。你可以使用 print 语句输出 tensor 的大小来进行调试。如果问题仍然存在，请提供更多的代码和错误信息，以便我更好地帮助你解决问题。

Sizes of tensors must match except in dimension 1. Expected size 2 but got size 1 for tensor number 1 in the list.

This error message typically occurs when you are trying to perform an operation that requires tensors of the same size, but one or more of the tensors in the list has a different size than expected. In particular, the error message suggests that the tensors have dimensions greater than 1, and that the only dimension in which they can differ is the first dimension (i.e., the batch size).

For example, suppose you have a list of tensors representing the inputs to a neural network, and you want to concatenate them along the second dimension (e.g., to combine two 1D feature vectors into a single 2D feature matrix). If the tensors have different sizes along the second dimension, you will get this error message.

To resolve this issue, you should check the sizes of the tensors in the list and make sure they are consistent with the operation you are trying to perform. If necessary, you may need to reshape or pad the tensors to ensure that they have the same size.