Factored out some code in the `image-segmentation` pipeline.
Author: Narsil
Pull request: yes

# What does this PR do?

Modifies the pipeline's internal a little.

- Hopefully less code, so easier to read.

- Removed the "blank mask" when nothing gets detected. Sending `[]` instead
  is more obvious IMO (and used to be the behavior, so making it non breaking change).
  
- Changed the default of `subtask` to `None` instead of `semantic`. `panoptic` used to be the default, and I merely adopted the same behavior. If a user specifies `subtask=x` we definitely try to use it's task and error out when it's impossible. But when nothing gets passed (should be the most common case), then we try **in order** `"panoptic", "instance", , and "semantic"`. Since roughly they are in decreasing order of expressiveness.

- Added `Detr` as an exception to sending empty lists in the randomly generated
  models (Like maskformer, it *can* return nothing because it has an empty slot
  it seems)
  
- Renamed `task` to `subtask` since `pipeline(task="image-segmentation")` already exists and would be impossible to use from `pipeline` directly. I feel `subtask` is appropriate but welcome any suggestions there.

TODO : figure out the real bug that makes `panoptic` on the tiny detr:

Figured it out. Not sure what the original code was doing but it was outputting two masks for LABEL_215.
What happens is that `pred_masks` is a tensor of shape `(2, H, W)` where `2` is the number of class queries.
Now we only have one mask because we have for all pixels `pred_masks[0, :, :] == pred_masks[1, :, :]`. So when we do
`pred_masks.argmax(dim=0)` to get the class they are associated with, everything gets attributed to class 0, and no pixel to class 1.
Since mask 1 is empty, we exclude it (which is good).

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Comments:
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Okay, I've pinpointed out the filter that filters OUT the prediction for panoptic:
(https://huggingface.slack.com/archives/D03VB3U5CVC/p1666108719919289)
36f52e9593
(https://huggingface.slack.com/archives/D03VB3U5CVC/p1666108795507369)
It's this line specifically : https://github.com/huggingface/transformers/pull/19262/files#diff-826e0e0303aa4a12e527884e4dc548fe8debb137905260e42ec8b4331c9c2c63R199
(https://huggingface.slack.com/archives/D03VB3U5CVC/p1666108939394469)
For the random model, the predictions are rather low (score == 0.004) and so >0.5 filter them out, basically loosing the predictions right there.I'm not sure who is correct, but as mentionned earlier, if semantic is saying something, I feel like panoptic without any thresholds should ALWAYS output at least as much.

I think the >0.5 comes from the expectations that sigmoid would have put the range of probs into something normalized and make sure something was in the >0.5 range.I don't really have the time to figure out where the logic fails (if it does) ?

Without this line I recover a `LABEL_215` label in the `small_model_pt` test (not two though) but with the same score, which is encouraging IMO.

@alaradirik @amyeroberts This PR is ready for review.

Don't hesitate to voice any concern over the changes. This mostly code cleanup. Except for the empty mask being removed (which wasn't the behavior before, and is not tested against).