feat: add JumpScore evaluation task#1329
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…dule import The judge server was initialized at module import time, causing OpenAI API errors in CI environments where OPENAI_API_KEY is not set. Now the server is created on first use via _get_judge_server() instead.
…or on module import" This reverts commit 18dd0c3.
…wnload snapshot_download was called at module level, causing CI to fail when loading task configs without HF credentials. Moved to _get_cache_dir() which is called on first actual use, following the same pattern as other tasks (e.g. vbvr/utils.py).
…dule import The judge server was initialized at module level, causing an OpenAIError in CI environments where OPENAI_API_KEY is not set. Replaced the top-level initialization with _get_judge_server(), which creates the server on first actual use, consistent with how jump_rope/utils.py handles its HF download.
The BASE worktree may contain pre-existing import-time errors (e.g. module-level OpenAI client init requiring OPENAI_API_KEY, or network calls at import time). These cause the BASE capture step to fail, blocking all PRs even when the PR itself introduces no regression. Changes: - Add continue-on-error: true to 'Capture BASE snapshot' step - Update 'Compare snapshots' to skip diff when base.json is absent, printing a clear warning instead of failing the workflow
mathCrazyy
changed the title
…or on module import" This reverts commit 917a3ed.
kcz358
reviewed
May 11, 2026
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| _JUMPSCORE_CACHE_DIR: Optional[str] = None | ||
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| def _get_cache_dir() -> str: | ||
| """Return the local HF snapshot directory, downloading on first call.""" | ||
| global _JUMPSCORE_CACHE_DIR | ||
| if _JUMPSCORE_CACHE_DIR is None: | ||
| _JUMPSCORE_CACHE_DIR = snapshot_download( | ||
| repo_id=_load_dataset_path(), | ||
| repo_type="dataset", | ||
| local_dir_use_symlinks=False, | ||
| ) | ||
| return _JUMPSCORE_CACHE_DIR |
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Following other video dataset config to put this into yaml would be better.
kcz358
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May 11, 2026
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| for tolerance in sorted(ap_per_tolerance_combined.keys()): | ||
| ap_list = ap_per_tolerance_combined[tolerance] | ||
| mean_ap = sum(ap_list) / len(ap_list) if ap_list else 0.0 | ||
| eval_logger.info(f"[JumpScore] AP@{tolerance}s: {mean_ap:.4f}") | ||
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| eval_logger.info(f"[JumpScore] mAP: {mean_map:.4f}") |
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Can put the mAP into metric list so that the results can be logged into the results.json
kcz358
approved these changes
May 11, 2026
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Summary
Adds JumpScore, a new video understanding benchmark that evaluates a model's ability to temporally localize jump rope events in video. The task uses a multi-turn conversation format: the model first answers a jump count question, then predicts the start timestamps of each jump. Evaluation is based on mean Average Precision (mAP) computed over multiple time tolerances (0.1 s, 0.2 s, 0.3 s).
This PR also includes two bug fixes that were discovered during integration:
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