DetrFeatureExtractor post_process not rescaling bboxes as expected
Author: nateraw
Pull request: no

## Environment info
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- `transformers` version:master
- Platform:Google Colab
- Python version:3.7
- PyTorch version (GPU?):1.8.1
- Tensorflow version (GPU?):N/A
- Using GPU in script?:N/A
- Using distributed or parallel set-up in script?:N/A

### Who can help
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@NielsRogge 
## Information

Model I am using (Bert, XLNet ...): `DetrForObjectDetection`

The problem arises when using:
* [ ] the official example scripts: (give details below)
* [x] my own modified scripts: (give details below)

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
* [x] my own task or dataset: (give details below)

Colab Below

## To reproduce

Steps to reproduce the behavior:

<a href="https://colab.research.google.com/gist/nateraw/b844f1f5118abd05c09a077fdec75dd3/detr-resize-issue.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>

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## Expected behavior

<!-- A clear and concise description of what you would expect to happen. -->

I would expect for `feature_extractor.post_process` to rescale the bounding boxes so they match the input images. Right now they seem to be scaled differently.

For example - the following should create `processed_outputs` that contain bbox values that are ready to be plotted along with the original image.

```python
import PIL

import torch
from transformers import DetrFeatureExtractor, DetrForObjectDetection

feature_extractor = DetrFeatureExtractor.from_pretrained('facebook/detr-resnet-50')
model = DetrForObjectDetection.from_pretrained('facebook/detr-resnet-50')
images: List[PIL.Image.Image]) = ...  # Some list of PIL images

inputs = feature_extractor(images, return_tensors='pt')
outputs = model(**inputs)
img_sizes = torch.tensor([im.size for im in images])
processed_outputs = feature_extractor.post_process(outputs, img_sizes)
```

:thought_balloon: - One thought I had was that I'm not sure if I'm preparing the `img_sizes` tensor correctly above. 

Comments:
False alarm...img sizes need to be flipped.

```
# ...

img_sizes = torch.tensor([tuple(reversed(im.size)) for im in images])

# ...
```