Sizes of tensors must match except in dimension 1. Expected size 1024 but got size 512 for tensor number 1 in the list

这个错误通常会在使用 PyTorch 进行张量操作时出现，它表示在操作中使用的张量尺寸不匹配。具体来说，这个错误表示第一个张量的第二个维度是 1024，而第二个张量的第二个维度是 512，这两个张量不能进行操作。

解决这个问题的方法是确保在进行张量操作之前，所有参与操作的张量的尺寸都是一致的。你可以使用 `tensor.size()` 函数来检查张量的尺寸，然后根据需要使用 `tensor.view()` 或者 `tensor.reshape()` 函数来调整张量的尺寸。如果需要对两个张量进行操作，可以使用 `torch.unsqueeze()` 函数来扩展张量的维度，以匹配另一个张量的维度。

相关问题

RuntimeError: Sizes of tensors must match except in dimension 1. Expected size 104 but got size 512 for tensor number 1 in the list.

这个错误通常是由于在进行某些操作时，两个张量的尺寸不匹配导致的。具体来说，这个错误信息中显示的是在第1个张量的第1个维度上期望尺寸为104，但实际上尺寸为512。

你需要检查你的代码，找出哪些张量在进行操作时尺寸不匹配，并且将它们的尺寸进行调整，以使它们能够在这个操作中匹配。你可以使用 PyTorch 提供的一些调整张量尺寸的函数，如 view() 或 reshape()。

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.