The size of tensor a (10) must match the size of tensor b (196) at non-singleton dimension 1

这个错误提示意味着在某个操作中，两个张量的形状不兼容。具体地说，两个张量的形状必须在某个非单例维度上匹配才能执行该操作。在你的情况下，张量a的大小为10，而张量b的大小为196，它们在第1个维度上的大小不匹配。

要解决这个问题，你需要检查代码中涉及的这两个张量的形状，并确保它们在所有需要匹配的维度上具有相同的大小。你可以使用`torch.Size()`或者`.size()`方法来获取张量的形状，然后进行比较。你还可以使用PyTorch提供的一些操作来改变张量的形状，例如`.view()`或`.reshape()`方法，以满足您的需求。

相关问题

The size of tensor a (256) must match the size of tensor b (196) at non-singleton dimension 3

这个错误提示是由于两个张量的维度不匹配导致的。在这种情况下，张量a的第3个维度的大小为256，而张量b的第3个维度的大小为196，它们不相等，因此会出现错误。

为了解决这个问题，你可以考虑调整张量a和张量b的维度，使它们在第3个维度上具有相同的大小。你可以使用一些张量操作函数来实现这个目标，例如torch.reshape()或torch.transpose()。

下面是一个示例，展示了如何调整张量a和张量b的维度，使它们在第3个维度上具有相同的大小：

import torch

# 假设a和b是两个张量
a = torch.randn(2, 3, 256, 4)
b = torch.randn(2, 3, 196, 4)

# 调整张量a的维度，使其在第3个维度上与张量b的大小相匹配
a = a[:, :, :196, :]

# 或者调整张量b的维度，使其在第3个维度上与张量a的大小相匹配
b = b[:, :, :256, :]

# 现在张量a和张量b在第3个维度上具有相同的大小

请注意，具体的调整方法取决于你的数据和需求，你可以根据实际情况选择适合的方法来调整张量的维度。

The size of tensor a (10) must match the size of tensor b (3) at non-singleton dimension 1

The error message "The size of tensor a (10) must match the size of tensor b (3) at non-singleton dimension 1" indicates that the dimensions of tensor a and tensor b do not match at dimension 1, which prevents the operation from being completed. It seems that the number of elements in tensor a at dimension 1 is 10, while the number of elements in tensor b at dimension 1 is 3.

To fix this issue, you can either resize one of the tensors to match the other tensor's dimension at dimension 1, or reshape one of the tensors to have a different number of dimensions.

Here are some possible solutions:

1. Reshape tensor a: You can reshape tensor a to match the number of elements in tensor b at dimension 1. For example, if tensor a has a shape of (10, 5) and tensor b has a shape of (3, 5), you can reshape tensor a to have a shape of (3, 2, 5) using the reshape() function.

2. Resize tensor b: Alternatively, you can resize tensor b to match the number of elements in tensor a at dimension 1. For example, if tensor a has a shape of (10, 5) and tensor b has a shape of (3, 5), you can resize tensor b to have a shape of (10, 5) using the resize() function.

3. Verify the input data: Double-check the input data for tensor a and tensor b to ensure that they have the correct dimensions and sizes.