File size: 3,950 Bytes
e88d4d4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | # VideoBenchEval
Multiple-choice evaluation of OnlineLong checkpoints (VideoLLaMA3-7B + qbase token
compressor) on **Video-MME**, **MLVU (dev)** and **LongVideoBench (val)**.
The model code comes from the training repo, pinned as a submodule at
`third_party/OnlineLong` (branch `ft`, commit `f76e08c` "qbase timestemps fix": the first
commit with per-segment time marks, which `phase1_qbase_internvid` was trained with).
## Setup
```bash
git submodule update --init
bash scripts/setup_env.sh # .venv: python 3.12, torch 2.9.1+cu126, flash-attn 2.8.3 built from source
```
Datasets are expected under `/workspace/chicheng/datasets` (`DATA_ROOT` to change):
| benchmark | videos | annotation | questions |
|---|---|---|---|
| Video-MME | `videomme/data/*.mp4` | `videomme/videomme/test-*.parquet` | 2700 (900 videos) |
| MLVU dev | `mlvu/*.mp4` | `mlvu/mlvu/test-*.parquet` | 2174 (7 MCQ tasks) |
| LongVideoBench val | `longvideobench/videos/*.mp4` | `longvideobench/lvb_val.json` | 1337 |
## Run
```bash
bash scripts/run_budgets.sh # B = 1k/2k/4k/8k/16k, one run each + summary table
bash scripts/run_eval.sh # one budget (BUDGET, default 53820), all GPUs, all 3
MODEL=path/or/hub_id GPUS=0,1 BENCHMARKS="mlvu" bash scripts/run_eval.sh
```
All budgets run in one pass: each video is decoded once (for the largest budget; smaller
budgets take an evenly spaced subset of those frames) and encoded once per frame size;
the questions of a video share one prefill of the video prefix (KV cache). The cached
prefill is not bit-identical to a single full prefill (bf16), which flips ~2% of
near-tie answers.
Outputs go to `results/<run>/<benchmark>/shard*.jsonl` (one line per question: response,
parsed letter, frame / vision-token counts) and `results/<run>/scores.json`. Re-running
the same command resumes; `python -m vbe.score results/<run>` re-scores.
## Protocol
Budgeted input (`vbe/budget.py`), B LLM-side vision tokens per video (`BUDGET`;
`run_budgets.sh` sweeps 1024 / 2048 / 4096 / 8192 / 16384):
1. Frames: decided by B. 1 fps, at most `B // 64 * 8` frames (128 ... 2048); longer videos
are decoded at ~`max_frames / duration` fps directly in ffmpeg. The target range is
0.2-1 fps; videos where B forces fps < 0.2 are flagged `fps_ok: false` per record.
2. One resolution for all frames: `min(native, 2048, max(256, B // T))` tokens per frame.
If `T * hw <= B`, every frame goes to the LLM uncompressed (`raw`).
3. Otherwise the Phase-1 adaptive segmenter cuts the video into `T // 4 + 1` groups of
1-8 frames. Group variation = mean of consecutive-frame cosine distances of the mean
encoder feature (1-frame groups: +inf, compressed last); groups are compressed (-> 64 qbase tokens) from the lowest variation
up until the video fits (`hybrid`).
4. If compressing every adaptive group still does not fit, uniform 8-frame groups, all
compressed (`forced8`).
Compressed groups use the checkpoint's projector; raw frames use the ORIGINAL
VideoLLaMA3 projector (`scripts/extract_orig_projector.py`; encoder and LLM are
bit-identical). LLM layout in time order: raw frame `Time 12.0s:<hw rows>,`, compressed
group `Time:{a}s-{b}s:<|compression_start|><64 rows><|compression_end|>`.
`tests/test_equivalence.py` checks the two extremes token-for-token: all-raw == plain
VideoLLaMA3 with the original projector, all-compressed == the Phase-1 training path.
Prompts and answer parsing: official VideoLLaMA3 evaluation
(`VideoLLaMA3/evaluation/benchmarks/*.py`), no subtitles. Greedy, 16 new tokens.
Unparseable / unreadable / OOM count as wrong (`no_answer`, `errors` in scores.json).
Reported: Video-MME overall + short/medium/long; MLVU M-Avg + per task; LongVideoBench
val overall + per duration group; every benchmark also per layout mode.
Not covered: subtitle variants, Video-MME / MLVU test sets, LongVideoBench test (no labels).
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