There were no tensor arguments to this function (e.g., you passed an empty list of Tensors), but no fallback function is registered for schema aten::_cat.

这个错误提示表明你在调用`torch.cat()`函数时传递了一个空的张量列表，导致没有可用的张量作为参数。

`torch.cat()`函数用于将多个张量在指定维度上进行拼接。它需要至少一个张量作为参数，并且要求拼接的张量在其他维度上的尺寸必须一致。

请确保你传递给`torch.cat()`函数的张量列表不为空，且每个张量的形状在指定拼接维度以外的其他维度上尺寸一致。

以下是一个示例，展示如何使用`torch.cat()`函数将两个张量在行维度上进行拼接：

import torch

tensor1 = torch.tensor([[1, 2], [3, 4]])
tensor2 = torch.tensor([[5, 6]])

result = torch.cat([tensor1, tensor2], dim=0)
print(result)

输出结果为：

tensor([[1, 2],
        [3, 4],
        [5, 6]])

请检查你的代码，确保你传递给`torch.cat()`函数的张量列表不为空，并且每个张量的形状在指定拼接维度以外的其他维度上尺寸一致。如果问题仍然存在，请提供更多的代码细节以便我能更好地帮助你解决问题。

相关问题

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.

RuntimeError: Sizes of tensors must match except in dimension 1. Expected size 128 but got size 129 for tensor number 1 in the list.

这个错误提示表明在一个张量列表中，第一个张量的第一维大小为128，而第二个张量的第一维大小为129，这两个张量的第一维大小不匹配，导致了运行时错误。解决这个问题的方法是确保所有张量的第一维大小相同。

以下是一个示例代码，用于演示如何解决这个问题：

import torch

# 创建两个张量，第一个张量的第一维大小为128，第二个张量的第一维大小为129
tensor1 = torch.randn(128, 10)
tensor2 = torch.randn(129, 10)

# 将这两个张量放入一个张量列表中
tensor_list = [tensor1, tensor2]

# 检查张量列表中的所有张量的第一维大小是否相同
for i in range(1, len(tensor_list)):
    if tensor_list[i].size(0) != tensor_list[0].size(0):
        # 如果有张量的第一维大小不同，则打印错误信息并退出程序
        print(f"Error: tensor {i+1} has size {tensor_list[i].size(0)} in dimension 1, but tensor 1 has size {tensor_list[0].size(0)} in dimension 1.")
        exit()

# 如果所有张量的第一维大小相同，则继续执行程序
print("All tensors have the same size in dimension 1.")