Fix MaskFormerImageProcessor.post_process_instance_segmentation
# What does this PR do?
Fixes the `post_process_instance_segmentation` method of `MaskFormerImageProcessor`. This issue mainly affects Mask2Former as it uses MaskFormerImageProcessor and there aren't any MaskFormer models trained on instance segmentation datasets. 

Unlike panoptic segmentation post-processing, the final score of each binary mask proposal is calculated by multiplying the mask proposal score with the class score. `mask_threshold` and `overlap_mask_area_threshold` arguments are not needed anymore, I can either add a warning to deprecate them or leave it as it is for now.

Post-processed results of the `mask2former-swin-small-coco-instance` model inference:
![download](https://user-images.githubusercontent.com/8944735/214045581-d177f24b-a7c1-4ec6-9d12-453b8300eb92.png)

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_The documentation is not available anymore as the PR was closed or merged._<|||||>> Thanks for fixing! Should we add a corresponding test for it, which verifies the postprocessed results?

I added a test but Mask2Former, unlike MaskFormer, outputs segmentation maps of shape (96, 96) instead of the preprocessed input size for efficiency. They scale the mask logits to the preprocessed image size during postprocessing (same for semantic and panoptic segmentation), even if no` target_sizes` is passed. I think it'd better to add an image processor for Mask2Former as its post-processing requires additional scaling.

What do you think @NielsRogge @sgugger?<|||||>If postprocessing is different, then it indeed requires its own image processor class.