Image Segmentation pipeline
Author: mishig25
Pull request: yes

# What does this PR do?
## TLDR
Implements `image-segmentation` pipeline (for [DetrForSegmentation](https://huggingface.co/transformers/model_doc/detr.html#detrforsegmentation) atm). 
## API specifications
Input: image source (identical to the input of `image-classification` & `object-detection` pipelines)
Output:
```python
List(Dict(
    mask: str, # base64 str
    label: float,
    score: int,
))
```

### Two design choices I've made and would like to discuss (& modify if needed):
1. Output `png_string` has masks information using same mechanism as COCO panoptic segmentation annotations. See section `4. Panoptic Segmentation` from https://cocodataset.org/#format-data. Paraphrasing a bit:
> per-pixel segment ids are stored in the PNG string. Each segment is assigned a unique id. Unlabeled pixels (void) are assigned a value of 0. Note that when you load the PNG as an RGB image, you will need to compute the ids via ids=R+G*256+B*256^2.
2. Image segmentation pipeline accepts `subtask` arg. There are different variations of segmentation task (semantic, instance, panoptic, etc. see image below). If a model doesn't implement requested subtask, it gets defaulted to what's available. See example below:
https://github.com/huggingface/transformers/blob/dd5c2697f129988bd9c70b52555e49dd32c78bd2/src/transformers/models/detr/feature_extraction_detr.py#L738-L739
<img width="400" src="https://user-images.githubusercontent.com/48327001/126451023-4edd68e4-c552-422a-9765-297a470d36c6.jpg">

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#### Update 1:
Updated output shape:
```python
List(Dict(
    mask: str, # base64 str
    label: float,
    score: int,
))
```
The goal for any segmentation architecture is to implement the most detailed version of segmentation subtask they can so that other subtasks can be reconstructed (if needed). For example, if an architecture post_process_segmentation method implements part-aware panoptic, other subtasks (including semantic, instance, etc.) can be reconstructed from part-aware panoptic output since all the details needed are in there

Comments:
Please let me know if I should merge this PR @Narsil @NielsRogge @LysandreJik 

It's gtg for me.