Add preserve_border flag to vjepa2_preprocessor (default off) to avoid extra border trimming during eval

[Lightet]

Summary

Introduce a boolean flag preserve_border in vjepa2_preprocessor (default: False).
When set to True, the eval resize step uses short_side_size = crop_size instead of the ImageNet-style crop_size * 256/224, which otherwise guarantees extra border pixels are trimmed by the subsequent CenterCrop.

Default behavior is unchanged.

Motivation

The current eval transform:

short_side_size = int(crop_size * 256 / 224)
Resize(short_side_size) → CenterCrop(crop_size) → ClipToTensor → Normalize

always resizes the short side to a size larger than the crop_size(e.g., 256→292) and then center-crops back to crop_size, discarding border pixels.
Some downstream video tasks are sensitive to peripheral content. preserve_border=True avoids this fixed extra trimming while keeping the output shape the same.

Change

• Add preserve_border: bool = False to vjepa2_preprocessor.
• If preserve_border is True, set short_side_size = crop_size; otherwise keep the existing *256/224 rule.

Minimal diff
The key change is in evals/video_classification_frozen/utils.py:

-    short_side_size = int(crop_size * 256 / 224)
+    short_side_size = int(crop_size * 256 / 224) if not preserve_border else crop_size

Usage

# Old behavior (default, for strict reproduction of prior results)
aug = vjepa2_preprocessor(crop_size=256)

# Preserve borders on the short side (avoid the extra upscaling/crop)
aug = vjepa2_preprocessor(crop_size=256, preserve_border=True)

Impact

• Backward compatible by default: leaving preserve_border=False keeps identical results.
• Opt-in (True) may slightly shift metrics due to less trimming; final tensor shapes remain [C, T, crop_size, crop_size].

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