How to Fix RuntimeError: stack expects each tensor to be equal size, but got [4] at entry 0 and [3] at entry 1

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RuntimeError: stack expects each tensor to be equal size, but got [4] at entry 0 and [3] at entry 1 error occurs when you “try to stack tensors that have different shapes using torch.stack() method.” In PyTorch, all tensors to be stacked must have the same shape.

Reproduce the error

import torch

tensor_a = torch.Tensor([1, 2, 3, 4])
tensor_b = torch.Tensor([5, 6, 7])
stacked_tensor = torch.stack((tensor_a, tensor_b), dim=0)

print(stacked_tensor)

Output

RuntimeError: stack expects each tensor to be equal size, 
              but got [4] at entry 0 and [3] at entry 1

Since their shapes are not equal, attempting to stack them will result in the RuntimeError.

How to fix the error

Here are the three ways to fix the error:

  1. Equalize the Shapes
  2. Using the torch.cat() method for Different Shapes
  3. Pad to Match Shapes

Solution 1: Equalize the shapes

Make sure all tensors have the same shape before stacking them.

import torch

tensor_a = torch.Tensor([1, 2, 3])
tensor_b = torch.Tensor([4, 5, 6])

stacked_tensor = torch.stack((tensor_a, tensor_b), dim=0)

print(stacked_tensor)

Output

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

Solution 2: Using the torch.cat() method for Different Shapes

If you want to concatenate tensors of different shapes, you can use the torch.cat() instead of torch.stack(). However, they must still match in dimensions other than the one you’re concatenating along.

import torch

tensor_a = torch.Tensor([1, 2, 3, 4])
tensor_b = torch.Tensor([5, 6, 7])
concatenated_tensor = torch.cat((tensor_a, tensor_b), dim=0)

print(concatenated_tensor)

Output

tensor([1., 2., 3., 4., 5., 6., 7.])

Solution 3: Pad to Match Shapes

In some scenarios, you might want to pad smaller tensors to make their shapes match before stacking.

import torch

tensor_a = torch.Tensor([1, 2, 3, 4])
tensor_b = torch.Tensor([5, 6, 7])
tensor_b_padded = torch.cat((tensor_b, torch.Tensor([0])), dim=0)
stacked_tensor = torch.stack((tensor_a, tensor_b_padded), dim=0)

print(stacked_tensor)

Output

tensor([[1., 2., 3., 4.],
        [5., 6., 7., 0.]])

Choose the best approach based on your specific use case.

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