RuntimeError: stack expects each tensor to be equal size, but got [3, 256, 305] at entry 0 and [3, 470, 512] at entry 1

This error typically occurs when using the `torch.stack()` function to concatenate tensors with different sizes along a particular dimension. In your case, the error message suggests that you are trying to stack tensors of size `[3, 256, 305]` and `[3, 470, 512]`, which have different dimensions in the second and third axes.

To fix this issue, you need to ensure that all tensors you want to stack have the same size along the specified dimension. You can achieve this by resizing or cropping the tensors to have the same dimensions before using `torch.stack()`.

Here's an example of how you can resize tensors to make them stackable:

import torch

# Assuming `tensor1` has size [3, 256, 305] and `tensor2` has size [3, 470, 512]

# Resize tensor1 to match the size of tensor2
tensor1_resized = torch.nn.functional.interpolate(tensor1, size=(470, 512), mode='bilinear', align_corners=False)

# Now you can stack the resized tensor1 and tensor2
stacked_tensors = torch.stack([tensor1_resized, tensor2], dim=0)

Make sure to adjust the resizing technique according to your specific requirements.

By ensuring that the tensors have the same size along the stacking dimension, you should be able to resolve the `RuntimeError` related to `torch.stack()`.