What does this PR do?

In certain scenarios, the evaluation loop concatenates tensors, making them unusable. For instance, in the object detection evaluation loop, the label structure is as follows:

batch_1 = [
    {
        'size': tensor([ 771, 1333]),
        'image_id': tensor([1592]),
        'class_labels': tensor([0]),
        'boxes': tensor([[0.2268, 0.6586, 0.1567, 0.1480]]),
        'area': tensor([23827.1230]),
        'iscrowd': tensor([0]),
        'orig_size': tensor([561, 970])
    },
    ...
]
batch_2 = [
    {
        'size': tensor([1333,  763]),
        'image_id': tensor([44]),
        'class_labels': tensor([0, 1]),
        'boxes': tensor([[0.4216, 0.4584, 0.3794, 0.1979], [0.4216, 0.4584, 0.3794, 0.1979]]),
        'area': tensor([76371.3984, 76371.3984]),
        'iscrowd': tensor([0, 0]),
        'orig_size': tensor([926, 530])
    },
    ...
]

Each dictionary represents one image, with all its bounding boxes.

The problem arises when the trainer runs the evaluation loop and must concatenate each result. It concatenates the labels of every evaluation batch, using the internal nested_concat.

The result of the nested concatenation of these labels will look like this:

[
    {
        'size': tensor([ 771, 1333, 1333,  763]),
        'image_id': tensor([1592, 44]),
        'class_labels': tensor([0, 0, 1]),
        'boxes': tensor([[0.2268, 0.6586, 0.1567, 0.1480], [0.4216, 0.4584, 0.3794, 0.1979], [0.4216, 0.4584, 0.3794, 0.1979]]),
        'area': tensor([23827.1230, 76371.3984, 76371.3984]),
        'iscrowd': tensor([0, 0]),
        'orig_size': tensor([561, 970, 926, 530])
    },
    ...
]

This results in the concatenation of all boxes, making it impossible to distinguish which boxes belong to each image.

This PR introduces an additional strategy for storing batches. By setting eval_do_concat_batches=False in the training arguments, batches will be stored as separate list items rather than being concatenated.

Fixes # (issue)
#25939
https://discuss.huggingface.co/t/possible-fix-for-trainer-evaluation-with-object-detection/72307
https://discuss.huggingface.co/t/add-metrics-to-object-detection-example/31213/12

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• Did you write any new necessary tests?

Who can review?

@muellerzr and @pacman100
@amyeroberts