DetrFeatureExtractor fails if do_normalize set to false
## Environment info
<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->

- `transformers` version: 4.11.0
- Platform: Ubuntu 20.04
- Python version: Python 3.8.10
- PyTorch version (GPU?):  1.8.1
- Tensorflow version (GPU?): -
- Using GPU in script?: yes 
- Using distributed or parallel set-up in script?: yes (but same error on local machine CPU normal script)

### Who can help
@LysandreJik 
@NielsRogge 

## Information

Model I am using:
Detr with COCOText-v2 dataset for text detection

The problem arises when using:
* [x ] my own modified scripts:
```
    def __getitem__(self, index):
        image_id = self.image_ids[index]
        image_file = self.image_info[index]
        annotations = self.ann_info[index]
        image_path = os.path.join(self.image_folder_path, image_file[0]['file_name'])
        image = Image.open(image_path).convert("RGB")

        target = {'image_id': image_id[0], 'annotations': annotations}
        encoding = self.feature_extractor(images=image, annotations=target, return_tensors="pt")
        pixel_values = encoding["pixel_values"].squeeze() # remove batch dimension
        target = encoding["labels"][0] # remove batch dimension
        return pixel_values, target
```

## To reproduce

Steps to reproduce the behavior:

1.` feature_extractor = DetrFeatureExtractor(format="coco_detection", do_resize=False, do_normalize=False, image_mean=[0.485, 0.456, 0.406], image_std=[0.229, 0.224, 0.225])`
2. `encoding = self.feature_extractor(images=image, annotations=target, return_tensors="pt")`
3.


transformers lib: DetrFeatureExtractor class line 584
```
        if pad_and_return_pixel_mask:
            # pad images up to largest image in batch and create pixel_mask
            max_size = self._max_by_axis([list(image.shape) for image in images])
```
```
  File "/home/felix/Desktop/memoresa-work-ml/Task_CV/Lightning_OCR/1_text_scene_detector/detr/dataloader.py", line 102, in __getitem__
    encoding = self.feature_extractor(images=image, annotations=target, return_tensors="pt")
  File "/home/felix/anaconda3/envs/work/lib/python3.8/site-packages/transformers/models/detr/feature_extraction_detr.py", line 584, in __call__
    max_size = self._max_by_axis([list(image.shape) for image in images])
  File "/home/felix/anaconda3/envs/work/lib/python3.8/site-packages/transformers/models/detr/feature_extraction_detr.py", line 584, in <listcomp>
    max_size = self._max_by_axis([list(image.shape) for image in images])
  File "/home/felix/anaconda3/envs/work/lib/python3.8/site-packages/PIL/Image.py", line 546, in __getattr__
    raise AttributeError(name)
AttributeError: shape
```



## Expected behavior



Thanks for reporting.

If you don't set `normalize` to `True`, then the images remain PIL images, i.e. they are not converted to Numpy arrays. If you then specify `pad_and_return_pixel_mask`, it cannot compute a shape.

I can add a check that converts the images to numpy arrays in any case. What's your use case for not normalizing the images? Do you already do this yourself?<|||||>@NielsRogge 
this would be great ! 

I do experiment currently with Detr for text detection and would check what i get back from my dataloader so in the normalize step the annotations ( targets ) would be also normalized which makes the reconstruction harder. 

So for example visualize/reconstruct some examples for test purpose with there bounding boxes before passing into the model.

I have also opened a thread about this use case if you have a bit time would be nice to get  your opinion :)
[Detr for text detection](https://discuss.huggingface.co/t/detection-transformer-detr-for-text-detection-in-documents/10396)<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

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