Best Practice: How to handle input data with multiple resolutions?

Hi, I am looking for some information regarding the best practice in PyTorch for data inputs into a model.

Problem description:
I work with satellite channels of different resolutions. Because of this one input may be half the size of another input (i.e. a 0.5 km resolution channel vs. a 1 km resolution channel). When I access the data in a generator, I load a tensor of inputs from a netcdf as well as a tensor targets (i.e. x and y). In order to account for the difference of resolutions, I have previously used numpy to created a concatenated array of inputs and an initial upsampling step within my U-Net model (mode nearest). However, I want to get away from using numpy, instead using Torch tensors to help reduce i/o. Correct me if I am wrong, but I believe the Torch tensors need the data to be the same size for a Torch tensor with I found the function torch.nn.functional.interpolate() which could be implemented before running the model (i.e. in the data generator), but I am not sure if it is proper to do this.

Thus, in the spirit of best practice, what is the best way to deal with data of multiple sizes?

I am just switching to PyTorch from Keras for the customizability with regards to GPU use, so forgive me if this is rather obvious.

You can think of torch tensors as np arrays.
I’m afraid tensors need to be same size to be concatenated of stacked.
Using numpy is a good practice as tensors can reuse the same memory address.
Usually data is padded. Pytorch doesn’t have a very optimized IO or there is no special tool.

If you want optimized IO you can try nvidia’s DALI.