stack expects each tensor to be equal size, but got [2, 256] at entry 0 and [2, 128] at entry 79
时间: 2024-01-08 10:02:08 浏览: 47
这个错误可能是由于你使用了`torch.stack`函数将不同形状的张量堆叠在一起,导致了张量的形状不匹配。
你需要确保每个张量的形状相同才能使用`torch.stack`函数。你可以使用`torch.cat`函数将不同形状的张量串联在一起,而不是堆叠它们。或者你可以在堆叠之前通过重塑或填充操作使它们具有相同的形状。
例如,如果你有一个形状为`[2, 256]`的张量和一个形状为`[2, 128]`的张量,你可以使用以下代码将它们串联在一起:
```
tensor1 = torch.randn(2, 256)
tensor2 = torch.randn(2, 128)
# 串联张量
concatenated_tensor = torch.cat([tensor1, tensor2], dim=1)
print(concatenated_tensor.shape) # 输出:torch.Size([2, 384])
```
这将创建一个形状为`[2, 384]`的张量,其中第二个维度是`256 + 128 = 384`。
希望这可以帮助你解决问题!
相关问题
stack expects each tensor to be equal size, but got [2, 2, 2] at entry 0 and [2, 2] at entry 1
这个错误是因为在使用`torch.stack()`函数时,输入的张量维度不一致导致的。`torch.stack()`函数要求输入的张量在除了拼接的维度外,其它维度大小必须相同。
例如,如果你想要将两个大小为(2,2)的张量在维度0上拼接起来,可以使用以下代码:
```python
import torch
a = torch.tensor([[1,2], [3,4]])
b = torch.tensor([[5,6], [7,8]])
c = torch.stack([a, b], dim=0)
print(c)
```
输出:
```
tensor([[[1, 2],
[3, 4]],
[[5, 6],
[7, 8]]])
```
如果要解决以上错误,需要检查输入的张量的维度是否一致,或者考虑使用其他的拼接函数。
stack expects each tensor to be equal size, but got [3, 256, 256] at entry 0 and [3, 256, 200] at entry 4
This error occurs when you try to stack tensors that have different sizes in the specified dimension. In this case, the error message suggests that you are trying to stack tensors of shape [3, 256, 256] and [3, 256, 200] along the first dimension (i.e., entry 0).
To fix this error, you need to ensure that all the tensors you want to stack have the same size along the specified dimension. You can either resize the tensors to be the same size or choose a different dimension to stack along.
For example, if you want to stack tensors along the second dimension (i.e., entry 1), you can use the following code:
```python
import torch
# create two tensors of different sizes
tensor1 = torch.randn(3, 256, 256)
tensor2 = torch.randn(3, 256, 200)
# stack tensors along the second dimension
stacked_tensor = torch.stack([tensor1, tensor2], dim=1)
```
This will create a stacked tensor of shape [3, 2, 256, 256], where the two input tensors are stacked along the second dimension.