`convert_to_contiguous` converts tuple to list, leading to expansion in `list_data_collate`

[function2-llx]

Describe the bug
When using CacheDataset with as_contiguous=True and the transform returns a tuple of tensors, the tuple will be converted to a list in convert_to_contiguous:

MONAI/monai/transforms/utils.py

Lines 1715 to 1716 in 8e99af5

	elif isinstance(data, Sequence) and not isinstance(data, bytes):
	return [convert_to_contiguous(i, **kwargs) for i in data] # type: ignore

Later, it will be expanded unexpectedly in list_data_collate:

MONAI/monai/data/utils.py

Lines 478 to 479 in 8e99af5

	elem = batch[0]
	data = [i for k in batch for i in k] if isinstance(elem, list) else batch

To Reproduce

import torch

from monai.data import CacheDataset, DataLoader
from monai import transforms as mt

def main():
    dataloader = DataLoader(CacheDataset(
        [0],
        mt.Lambda(lambda _: (torch.randn(1), torch.randn(2)))
    ))
    next(iter(dataloader))

if __name__ == '__main__':
    main()

Expected behavior
A tuple of batched tensors are returned from the dataloader.

Actual result

Loading dataset: 100%|█████████████████████████| 1/1 [00:00<00:00, 11715.93it/s]
> collate/stack a list of tensors
> E: stack expects each tensor to be equal size, but got [1] at entry 0 and [2] at entry 1, shape [torch.Size([1]), torch.Size([2])] in collate([tensor([2.3643]), tensor([-0.4322, -1.4578])])
Traceback (most recent call last):
  File ".../monai/data/utils.py", line 491, in list_data_collate
    ret = collate_meta_tensor(data)
          ^^^^^^^^^^^^^^^^^^^^^^^^^
  File ".../monai/data/utils.py", line 465, in collate_meta_tensor
    return default_collate(batch)
           ^^^^^^^^^^^^^^^^^^^^^^
  File "..../torch/utils/data/_utils/collate.py", line 265, in default_collate
    return collate(batch, collate_fn_map=default_collate_fn_map)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "..../torch/utils/data/_utils/collate.py", line 119, in collate
    return collate_fn_map[elem_type](batch, collate_fn_map=collate_fn_map)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "..../torch/utils/data/_utils/collate.py", line 162, in collate_tensor_fn
    return torch.stack(batch, 0, out=out)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: stack expects each tensor to be equal size, but got [1] at entry 0 and [2] at entry 1

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File ".../scripts/_local/downstream/dl.py", line 16, in <module>
    main()
  File ".../scripts/_local/downstream/dl.py", line 13, in main
    next(iter(dataloader))
  File "..../torch/utils/data/dataloader.py", line 633, in __next__
    data = self._next_data()
           ^^^^^^^^^^^^^^^^^
  File "..../torch/utils/data/dataloader.py", line 677, in _next_data
    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "..../torch/utils/data/_utils/fetch.py", line 54, in fetch
    return self.collate_fn(data)
           ^^^^^^^^^^^^^^^^^^^^^
  File ".../monai/data/utils.py", line 504, in list_data_collate
    raise RuntimeError(re_str) from re
RuntimeError: stack expects each tensor to be equal size, but got [1] at entry 0 and [2] at entry 1

MONAI hint: if your transforms intentionally create images of different shapes, creating your `DataLoader` with `collate_fn=pad_list_data_collate` might solve this problem (check its documentation).

[wyli]

good point, I think the convertion could have ret = type(data)(convert_to_contiguous(i, **kwargs) for i in data)