enable training mask2former and maskformer for transformers trainer
Author: SangbumChoi
Pull request: yes

# What does this PR do?

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Fixes # (issue)


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Models:

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Originally the output of loss_dict was like as follows
```
{'loss_mask': tensor([2.1010], device='cuda:1', grad_fn=<MulBackward0>), 'loss_dice': tensor([2.0065], device='cuda:1', grad_fn=<MulBackward0>), 'loss_cross_entropy': tensor(8.3828, device='cuda:1', grad_fn=<MulBackward0>), 'loss_mask_0': tensor([1.5738], device='cuda:1', grad_fn=<MulBackward0>), 'loss_dice_0': tensor([3.0623], device='cuda:1', grad_fn=<MulBackward0>), 'loss_cross_entropy_0': tensor(8.3828, device='cuda:1', grad_fn=<MulBackwa
```
which returns total loss as 1 dimension shape of tensor like tensor([]).
However, transformers trainer https://github.com/huggingface/transformers/blob/3cefac1d974db5e2825a0cb2b842883a628be7a0/src/transformers/trainer.py#L1772
requires float tensor type. From the [example](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/MaskFormer/Fine-tuning/Fine_tune_MaskFormer_on_an_instance_segmentation_dataset_(ADE20k_full).ipynb) you may notice that output loss was returning one dimensional shape.

The reason was         
num_masks_pt = torch.as_tensor([num_masks], dtype=torch.float, device=device)
returns one dimensional shape which can be also single integer tensor.

This will not make any difference of training/inference logic but enables to train with transformers trainer.

@muellerzr and @pacman100

Comments:
FYI, original mask2former/maskformer is also not scalar. However, this actually doesn't matter at the `loss.backward()`