The size of tensor a (5472) must match the size of tensor b (64) at non-singleton dimension 0

这个错误提示的意思是你在进行某个操作时，两个张量的尺寸在某个维度上不匹配。具体来说，你有两个张量a和b，它们的形状分别是(5472)和(64)。在某个操作中，你需要使用这两个张量，但是在操作的第0个维度上，它们的尺寸不一致。根据提示，你需要检查一下你的代码，找到这个操作所在的位置，并确保两个张量在该维度上的尺寸相同。你可以使用PyTorch的resize或reshape函数来调整张量的维度，以确保它们具有匹配的尺寸。

相关问题

The size of tensor a (100) must match the size of tensor b (10) at non-singleton dimension 0

这个错误提示意味着张量a和张量b在非单一维度0上的大小不匹配，因此无法完成操作。如果张量a的大小为100，张量b的大小为10，则无法在第0个维度上匹配它们。要解决这个问题，您需要确保张量a和张量b在所有维度上的大小都匹配，或者使用广播机制来匹配它们的大小。

以下是两种解决方法的示例代码：

1. 确保张量a和张量b在所有维度上的大小都匹配：

import torch

a = torch.randn(100, 5)
b = torch.randn(10, 5)

if a.size(0) != b.size(0):
    raise ValueError("The size of tensor a ({}) must match the size of tensor b ({}) at non-singleton dimension 0".format(a.size(0), b.size(0)))

# 进行操作

2. 使用广播机制来匹配张量a和张量b的大小：

import torch

a = torch.randn(100, 5)
b = torch.randn(10, 5)

# 使用广播机制来匹配张量a和张量b的大小
a = a.unsqueeze(1)  # 在第1个维度上添加一个维度
b = b.unsqueeze(0)  # 在第0个维度上添加一个维度
a = a.expand(-1, 10, -1)  # 在第1个维度上复制10次
b = b.expand(100, -1, -1)  # 在第0个维度上复制100次

# 进行操作

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.