| # Time-R1 baseline on ActivityForensics |
|
|
| This directory only adds data adapters; **all upstream Time-R1 code is unchanged** |
| (`finetune.py`, `src/time_r1/*`, etc.). Reward (single-span IoU + optional format) |
| is taken as-is from the original repo. |
|
|
| ## What gets adapted |
|
|
| ActivityForensics annotations are multi-segment (a video has K ≥ 1 forged |
| intervals). Time-R1 only models single-span temporal grounding, so each |
| `(video, segment)` pair is emitted as one training example with a fixed query: |
|
|
| > "an AI-manipulated segment" |
|
|
| Multi-segment GT is split per segment; all splits of the same video share the |
| same Qwen2.5-VL preprocessed cache via symlinks. |
|
|
| ## Setup |
|
|
| ```bash |
| cd /mnt/local-fast/zhangt/baselines/time_r1 |
| |
| # 1) Build Time-R1-format annotation JSON. |
| python data_forensics/build_forensics_json.py \ |
| --annot_dir /ces/zt/activityforensics/annot \ |
| --video_root /ces/zt \ |
| --output_dir ./dataset/forensics/annotation |
| # -> writes train.json / val.json |
| |
| # 2) Symlink the pre-encoded video tensors that forensics_grpo already produced. |
| python data_forensics/link_cache.py \ |
| --forensics_cache /mnt/local-fast/zhangt/forensics_grpo/<your_cache_dir> \ |
| --annotation_json_dir ./dataset/forensics/annotation \ |
| --output_dir ./dataset/forensics/preprocessed |
| # -> creates ./dataset/forensics/preprocessed/{train,eval}/<vid>_<sid>/... |
| |
| # 3) Launch training (edit MODEL_PATH at the top of the script first). |
| bash scripts/finetune/run_forensics.sh |
| ``` |
|
|
| `build_forensics_json.py` uses forensics_grpo's `data_loader` parser, so any |
| change to its annotation schema flows through automatically. Override |
| `FORENSICS_GRPO_ROOT=<path>` if forensics_grpo lives elsewhere. |
| |
| ## Files added |
| |
| - `data_forensics/build_forensics_json.py` - .txt → Time-R1 .json |
| - `data_forensics/link_cache.py` - cache layout symlinker |
| - `scripts/finetune/run_forensics.sh` - launch script |
|
|
