Enable multi-GPU in object detection
Author: SangbumChoi
Pull request: yes

# What does this PR do?

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Fixes https://github.com/huggingface/transformers/issues/33525

This is the concept of the PR that fundamentally fixes the following multi-GPU circumstances error. The reason why I wrote as `concept` is because torchmetrics does not accept multi-gpu problem + also I want to make some code more clean.

Main keypoint is that all the `Trainer` class accept the `prediction` and `labels` as nested tensor (e.g. batch size : 4, num_gpu : 2 -> length of 8). However in order to calculate proper evaluation metric it should be shape as `batch_size`. + We should always encourage to use `accelerate` as default.

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Comments:
@qubvel Hi Pavel, even though this is draft (which is finished but want to make some code clean) I want you to review about overall idea :) cc. @amyeroberts 

Thanks for opening this PR @SangbumChoi! 

What I would propose is creating a new script e.g. `run_object_detection_multigpu.py` which contains there changes. The reason for that is that all the other `run_xxx` scripts follow a standard pattern with the Trainer, and using `accelerator` to prepare things isn't a common pattern. This way, it highlights the difference in the two methods and enables easier comparison / pattern matching across scripts in the single GPU case. 

Otherwise, I think the changes here all looks great :D 

@amyeroberts I found that this flatten issue happens all over the detection pipeline (e.g. detr, groundingdino, deformable-detr, etc..). I think it would be better to add additional argument at `Trainer`!

BTW, as you suggested I will seperate the file to `multigpu.py` 👍🏼 

> @amyeroberts I found that this flatten issue happens all over the detection pipeline (e.g. detr, groundingdino, deformable-detr, etc..). I think it would be better to add additional argument at Trainer!

Agreed! I think @qubvel was working on something to enable this in trainer

Is there any progress on this issue? I would be much interested :)

@daniel-bogdoll Thanks for the interest. I think you can use current script but I will plan to support amy's suggestion.

[rank0]:   File examples/pytorch/object-detection/run_object_detection_multi_gpu.py", line 216, in compute_metrics
[rank0]:     logits=torch.tensor(batch_logits).squeeze(1), pred_boxes=torch.tensor(batch_boxes).squeeze(1)
[rank0]: RuntimeError: Could not infer dtype of dict

This happens because the loss is present in batch_logits as a dict. 

I am facing this error when trying to evaluate, did you also face any issue when trying to evaluate?


I was able to fix this by modifying the compute metrics as:
```
batch_size = batch[1].shape[0]
num_devices = len(target_sizes) // batch_size
nested_integer = len(batch) // num_devices
batch_logits, batch_boxes = torch.tensor(batch[1::nested_integer]), torch.tensor(batch[2::nested_integer])
output = ModelOutput(logits=batch_logits.view(1, -1, *(batch_logits.size()[2:])).squeeze(0), pred_boxes=batch_boxes.view(1, -1, *(batch_boxes.size()[2:])).squeeze(0))
```