The size of tensor a (32) must match the size of tensor b (224) at non-singleton dimension 2
时间: 2023-12-13 20:32:32 浏览: 68
这个错误提示意味着张量a和张量b在非单一维度2上的大小不匹配,因此无法完成操作。要解决这个问题,需要确保张量a和张量b在维度2上的大小相同。可以使用PyTorch中的view()函数来更改张量的形状,以确保它们在所需的维度上具有相同的大小。例如,可以使用以下代码将张量a的形状更改为(?, 32, ?),其中?表示任意大小的维度,以匹配张量b的形状(?, 224, ?):
```python
a = a.view(-1, 32, -1)
```
这将自动计算第一个和第三个维度的大小,以确保在维度2上具有相同的大小。然后,您可以执行所需的操作。
相关问题
The size of tensor a (32) must match the size of tensor b (512) at non-singleton dimension 1
The error message "The size of tensor a (32) must match the size of tensor b (512) at non-singleton dimension 1" indicates that there is a size mismatch between two tensors in dimension 1. In order to perform operations between tensors, their sizes must match along all dimensions except for the singleton dimensions.
To resolve this error, you can check the shape of tensor a and tensor b along dimension 1 and ensure they have the same size. If they don't have the same size, you may need to reshape or resize one or both tensors to match their sizes.
Related questions:
1. How can I check the shape of a tensor in PyTorch?
2. How do I resize or reshape a tensor in PyTorch?
3. What are singleton dimensions in tensors?
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.
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