Fixing image segmentation with inference mode.
Author: Narsil
Pull request: yes

# What does this PR do?

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It seems `detr` models are modifying tensors inplace, which is not allowed
by `inference_mode` context manager, effectively breaking `image-segmentation` for
`torch > 1.9`  users.

This PR proposes to override the context manager **only** in `image-segmentation` to `no_grad`
so the pipeline works again, without checking underlying model. Using a method to do the
switch.

Fixes # (issue)


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@mishig25  @LysandreJik

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Comments:
Fine with this solution, but a better one might actually to disable all those:

```python
        if torch.isinf(hidden_states.clone()).any() or torch.isnan(hidden_states.clone()).any():
            clamp_value = torch.finfo(hidden_states.dtype).max - 1000
            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) 
```

for inference to make inference mode possible. Those "value" clamping checks are IMO only really useful to allow fp16 training (not so much for fp16 inference) and thus we could disable them with an `if self.training` statement. 

@patil-suraj and I have somewhat unsuccesfully added lots of those statements to T5 in the hope of enabling fp16 training, which turned out to not work very well...with bfloat16 being available more for PyTorch those statements might not serve a good purpose anymore anyways.

=> So I would be fine with hiding them behind a `self.training`. 

Pinging @stas00 here as well to hear his opinion :-)

Agreed with @patrickvonplaten it would be better if we could keep ` inference_mode`  and work we the other fix.

This fix is fine for a quick patch (patch release is going to be made in the next couple of hours), and we can make a better one afterward. So I would merge this now :-)