VIPER reproduction: code, mini VIPER-19K pipeline, qualitative comparisons and eval
Browse files- .gitattributes +4 -0
- README.md +23 -9
- data/annotated.jsonl +0 -0
- data/annotated.pilot2k.jsonl +0 -0
- data/clips_train.jsonl +3 -0
- data/filtered_clips.jsonl +0 -0
- data/filtered_clips.pilot2k.jsonl +0 -0
- data/pairs.jsonl +0 -0
- data/pairs_candidates.jsonl +0 -0
- data/pairs_train.jsonl +0 -0
- data/pairs_val.jsonl +0 -0
- logs/fetch_wisa.log +30 -0
- logs/probe_31601035.log +9 -0
- logs/scaleup_1_annotate_31601075.log +201 -0
- logs/scaleup_1_annotate_31601197.log +365 -0
- logs/scaleup_2_pairs_31601200.log +650 -0
- logs/scaleup_3_precompute_31601202.log +479 -0
- scripts/eval_run.sbatch +37 -0
- scripts/fetch_wisa.py +74 -0
- scripts/full_run.sbatch +18 -23
- scripts/make_splits.py +18 -1
- scripts/push_to_hf.py +30 -0
- scripts/run_infer.sh +1 -1
- scripts/run_judge.sh +3 -1
- scripts/run_precompute.sh +4 -2
- scripts/scaleup_1_annotate.sbatch +40 -0
- scripts/scaleup_2_pairs.sbatch +42 -0
- scripts/scaleup_3_precompute.sbatch +24 -0
- viper/data/build_pairs.py +14 -0
- viper/data/filter_clips.py +68 -39
.gitattributes
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@@ -96,3 +96,7 @@ results/comparison/00001970__00001206/grid.mp4 filter=lfs diff=lfs merge=lfs -te
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results/comparison/00001970__00001206/reference.mp4 filter=lfs diff=lfs merge=lfs -text
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results/comparison/00001970__00001206/target_gt.mp4 filter=lfs diff=lfs merge=lfs -text
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results/comparison/00001970__00001206/viper.mp4 filter=lfs diff=lfs merge=lfs -text
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results/comparison/00001970__00001206/reference.mp4 filter=lfs diff=lfs merge=lfs -text
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results/comparison/00001970__00001206/target_gt.mp4 filter=lfs diff=lfs merge=lfs -text
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results/comparison/00001970__00001206/viper.mp4 filter=lfs diff=lfs merge=lfs -text
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data/annotated.jsonl filter=lfs diff=lfs merge=lfs -text
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data/clips_train.jsonl filter=lfs diff=lfs merge=lfs -text
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data/filtered_clips.jsonl filter=lfs diff=lfs merge=lfs -text
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data/pairs_candidates.jsonl filter=lfs diff=lfs merge=lfs -text
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README.md
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@@ -75,15 +75,29 @@ them is attributable to the reference stream alone.
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| `results/eval_loss_stage1.json` | Paired conditioning ablation: flow-matching loss with reference tokens vs zeroed, same sample/noise/timestep (36 measurements) |
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| `results/comparison_manifest.jsonl` | Index feeding `viper.eval` |
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-
###
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| `data/
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### Logs
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| `results/eval_loss_stage1.json` | Paired conditioning ablation: flow-matching loss with reference tokens vs zeroed, same sample/noise/timestep (36 measurements) |
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| `results/comparison_manifest.jsonl` | Index feeding `viper.eval` |
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### Our VIPER-19K rebuild
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Built from the full `csusupergear/WISA-80K-wan480p-16fps-81f` subset (276 shards,
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27543 clips) via `scripts/fetch_wisa.py` + `scripts/scaleup_{1,2,3}_*.sbatch`.
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The pilot column is the earlier 20-shard run, kept for reference.
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| Path | Pilot | Full | Stage of the pipeline |
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|---|---|---|---|
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| `data/videos/` | 2000 | 27543 | downloaded WISA wan480p clips |
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| `data/filtered_clips.jsonl` | 523 | 7711 | after clip filtering |
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| `data/annotated.jsonl` | 523 | 7693 | after 3-axis physics annotation |
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| `data/pairs_candidates.jsonl` | 996 | 15608 | after label bucketing |
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| `data/pairs.jsonl` | 64 | 934 | after Qwen3-VL-32B transferability filtering |
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| `data/pairs_train.jsonl` / `pairs_val.jsonl` | 23 / 9 | 553 / 74 | video-disjoint split |
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| `data/clips_train.jsonl` | — | 7568 | stage-1 clips minus held-out videos |
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What the filters cut, at full scale: 14762 near-static (`motion_score < 1.0`),
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4126 non-mechanical labels, 725 low quality, 219 shot transitions, 0 watermarked.
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The 32B judge kept 934 of ~9600 pairs it got through (9.7%).
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Only ~9600 of the 15608 candidates were judged: the judge runs at ~6 s/pair/rank
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and the 4h interactive limit cuts it off there. `--deadline_min` makes that stop
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graceful so the split still runs; raise it on a longer partition to judge the rest.
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### Logs
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data/annotated.jsonl
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The diff for this file is too large to render.
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data/annotated.pilot2k.jsonl
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data/clips_train.jsonl
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version https://git-lfs.github.com/spec/v1
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oid sha256:14ad8db9f5bc7618f7570cca39047baf5175610c4f90f8ba353ca3f0547e6d66
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size 13748928
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data/filtered_clips.jsonl
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data/filtered_clips.pilot2k.jsonl
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data/pairs.jsonl
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data/pairs_candidates.jsonl
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data/pairs_train.jsonl
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data/pairs_val.jsonl
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logs/fetch_wisa.log
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10/276 shards (skip 000010)
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20/276 shards (skip 000016)
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/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/scripts/fetch_wisa.py:37: DeprecationWarning: Python 3.14 will, by default, filter extracted tar archives and reject files or modify their metadata. Use the filter argument to control this behavior.
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tf.extractall(vdir)
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30/276 shards (ok 000022)
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40/276 shards (ok 000041)
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50/276 shards (ok 000048)
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60/276 shards (ok 000059)
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70/276 shards (ok 000069)
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80/276 shards (ok 000081)
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90/276 shards (ok 000090)
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100/276 shards (ok 000099)
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110/276 shards (ok 000108)
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120/276 shards (ok 000119)
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170/276 shards (ok 000170)
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180/276 shards (ok 000180)
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200/276 shards (ok 000199)
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220/276 shards (ok 000219)
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DONE shards=276 errors=0 clips=27543
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logs/probe_31601035.log
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batch-block7-00514
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GPU 0: NVIDIA A100-SXM4-80GB (UUID: GPU-efd9139e-b868-c723-1de6-d706dba2b434)
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GPU 1: NVIDIA A100-SXM4-80GB (UUID: GPU-b3fa51a5-71af-7f75-33f0-0f8d4bbf252c)
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GPU 2: NVIDIA A100-SXM4-80GB (UUID: GPU-4d14e0f1-134f-3f49-356f-d524ab725b3e)
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GPU 3: NVIDIA A100-SXM4-80GB (UUID: GPU-0e3562b7-2a44-f355-23a1-a3008889daec)
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GPU 4: NVIDIA A100-SXM4-80GB (UUID: GPU-0497d167-8ade-665a-7e59-b495b952c826)
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GPU 5: NVIDIA A100-SXM4-80GB (UUID: GPU-508aea10-2442-a9b1-067b-2940893e958f)
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GPU 6: NVIDIA A100-SXM4-80GB (UUID: GPU-113b3eb8-b4e5-06b6-5a28-93b612864403)
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GPU 7: NVIDIA A100-SXM4-80GB (UUID: GPU-84382381-48e2-9453-5ec9-2e02f83e50a2)
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logs/scaleup_1_annotate_31601075.log
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=== clip filtering (27543 clips on disk) ===
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0/27543 kept=1
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2000/27543 kept=523
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4000/27543 kept=1139
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6000/27543 kept=1678
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8000/27543 kept=2243
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10000/27543 kept=2768
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12000/27543 kept=3332
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14000/27543 kept=3912
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16000/27543 kept=4499
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18000/27543 kept=5063
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20000/27543 kept=5633
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22000/27543 kept=6187
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24000/27543 kept=6754
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26000/27543 kept=7275
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{
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"total": 27543,
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"missing": 0,
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"label": 4126,
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"quality": 725,
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"motion": 14762,
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"text": 0,
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"decode": 0,
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"cuts": 219,
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"kept": 7711
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}
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wrote 7711 clips -> data/filtered_clips.jsonl
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=== 3-axis annotation (Qwen3-VL-4B, 8 ranks) ===
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Traceback (most recent call last):
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File "<frozen runpy>", line 198, in _run_module_as_main
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File "<frozen runpy>", line 88, in _run_code
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Traceback (most recent call last):
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File "<frozen runpy>", line 198, in _run_module_as_main
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File "<frozen runpy>", line 88, in _run_code
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File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/viper/data/annotate.py", line 147, in <module>
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Traceback (most recent call last):
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File "<frozen runpy>", line 198, in _run_module_as_main
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File "<frozen runpy>", line 88, in _run_code
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File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/viper/data/annotate.py", line 147, in <module>
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File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/viper/data/annotate.py", line 147, in <module>
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Traceback (most recent call last):
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File "<frozen runpy>", line 198, in _run_module_as_main
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File "<frozen runpy>", line 88, in _run_code
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File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/viper/data/annotate.py", line 147, in <module>
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Traceback (most recent call last):
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File "<frozen runpy>", line 198, in _run_module_as_main
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File "<frozen runpy>", line 88, in _run_code
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File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/viper/data/annotate.py", line 147, in <module>
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main()
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main()
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main()
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main()
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File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/viper/data/annotate.py", line 97, in main
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File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/viper/data/annotate.py", line 97, in main
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File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/viper/data/annotate.py", line 97, in main
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File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/viper/data/annotate.py", line 97, in main
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main()
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File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/viper/data/annotate.py", line 97, in main
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model = Qwen3VLForConditionalGeneration.from_pretrained(
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model = Qwen3VLForConditionalGeneration.from_pretrained(
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| 61 |
+
model = Qwen3VLForConditionalGeneration.from_pretrained(
|
| 62 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 63 |
+
model = Qwen3VLForConditionalGeneration.from_pretrained(
|
| 64 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 277, in _wrapper
|
| 65 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 66 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 277, in _wrapper
|
| 67 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 68 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 277, in _wrapper
|
| 69 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 70 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 277, in _wrapper
|
| 71 |
+
model = Qwen3VLForConditionalGeneration.from_pretrained(
|
| 72 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 73 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 277, in _wrapper
|
| 74 |
+
Traceback (most recent call last):
|
| 75 |
+
File "<frozen runpy>", line 198, in _run_module_as_main
|
| 76 |
+
File "<frozen runpy>", line 88, in _run_code
|
| 77 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/viper/data/annotate.py", line 147, in <module>
|
| 78 |
+
return func(*args, **kwargs)
|
| 79 |
+
return func(*args, **kwargs)
|
| 80 |
+
return func(*args, **kwargs)
|
| 81 |
+
return func(*args, **kwargs)
|
| 82 |
+
^^^^^^^^^^^^^^^^^^^^^
|
| 83 |
+
^^^^^^^^^^^^^^^^^^^^^
|
| 84 |
+
^^^^^^^^^^^^^^^^^^^^^
|
| 85 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 5048, in from_pretrained
|
| 86 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 5048, in from_pretrained
|
| 87 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 5048, in from_pretrained
|
| 88 |
+
^^^^^^^^^^^^^^^^^^^^^
|
| 89 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 5048, in from_pretrained
|
| 90 |
+
return func(*args, **kwargs)
|
| 91 |
+
^^^^^^^^^^^^^^^^^^^^^
|
| 92 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 5048, in from_pretrained
|
| 93 |
+
main()
|
| 94 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/viper/data/annotate.py", line 97, in main
|
| 95 |
+
) = cls._load_pretrained_model(
|
| 96 |
+
model = Qwen3VLForConditionalGeneration.from_pretrained(
|
| 97 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 98 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 5432, in _load_pretrained_model
|
| 99 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 100 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 277, in _wrapper
|
| 101 |
+
return func(*args, **kwargs)
|
| 102 |
+
) = cls._load_pretrained_model(
|
| 103 |
+
^^^^^^^^^^^^^^^^^^^^^
|
| 104 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 5048, in from_pretrained
|
| 105 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 106 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 5432, in _load_pretrained_model
|
| 107 |
+
) = cls._load_pretrained_model(
|
| 108 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 109 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 5432, in _load_pretrained_model
|
| 110 |
+
) = cls._load_pretrained_model(
|
| 111 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 112 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 5432, in _load_pretrained_model
|
| 113 |
+
) = cls._load_pretrained_model(
|
| 114 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 115 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 5432, in _load_pretrained_model
|
| 116 |
+
caching_allocator_warmup(model, expanded_device_map, hf_quantizer)
|
| 117 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 6089, in caching_allocator_warmup
|
| 118 |
+
) = cls._load_pretrained_model(
|
| 119 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 120 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 5432, in _load_pretrained_model
|
| 121 |
+
caching_allocator_warmup(model, expanded_device_map, hf_quantizer)
|
| 122 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 6089, in caching_allocator_warmup
|
| 123 |
+
caching_allocator_warmup(model, expanded_device_map, hf_quantizer)
|
| 124 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 6089, in caching_allocator_warmup
|
| 125 |
+
caching_allocator_warmup(model, expanded_device_map, hf_quantizer)
|
| 126 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 6089, in caching_allocator_warmup
|
| 127 |
+
caching_allocator_warmup(model, expanded_device_map, hf_quantizer)
|
| 128 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 6089, in caching_allocator_warmup
|
| 129 |
+
index = device.index if device.index is not None else torch_accelerator_module.current_device()
|
| 130 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 131 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py", line 940, in current_device
|
| 132 |
+
caching_allocator_warmup(model, expanded_device_map, hf_quantizer)
|
| 133 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 6089, in caching_allocator_warmup
|
| 134 |
+
index = device.index if device.index is not None else torch_accelerator_module.current_device()
|
| 135 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 136 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py", line 940, in current_device
|
| 137 |
+
index = device.index if device.index is not None else torch_accelerator_module.current_device()
|
| 138 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 139 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py", line 940, in current_device
|
| 140 |
+
index = device.index if device.index is not None else torch_accelerator_module.current_device()
|
| 141 |
+
index = device.index if device.index is not None else torch_accelerator_module.current_device()
|
| 142 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 143 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py", line 940, in current_device
|
| 144 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 145 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py", line 940, in current_device
|
| 146 |
+
_lazy_init()
|
| 147 |
+
_lazy_init()
|
| 148 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py", line 319, in _lazy_init
|
| 149 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py", line 319, in _lazy_init
|
| 150 |
+
_lazy_init()
|
| 151 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py", line 319, in _lazy_init
|
| 152 |
+
index = device.index if device.index is not None else torch_accelerator_module.current_device()
|
| 153 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 154 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py", line 940, in current_device
|
| 155 |
+
_lazy_init()
|
| 156 |
+
torch._C._cuda_init()
|
| 157 |
+
torch._C._cuda_init()
|
| 158 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py", line 319, in _lazy_init
|
| 159 |
+
_lazy_init()
|
| 160 |
+
RuntimeError: No CUDA GPUs are available
|
| 161 |
+
RuntimeError: No CUDA GPUs are available
|
| 162 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py", line 319, in _lazy_init
|
| 163 |
+
torch._C._cuda_init()
|
| 164 |
+
RuntimeError: No CUDA GPUs are available
|
| 165 |
+
torch._C._cuda_init()
|
| 166 |
+
RuntimeError: No CUDA GPUs are available
|
| 167 |
+
_lazy_init()
|
| 168 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py", line 319, in _lazy_init
|
| 169 |
+
torch._C._cuda_init()
|
| 170 |
+
RuntimeError: No CUDA GPUs are available
|
| 171 |
+
torch._C._cuda_init()
|
| 172 |
+
RuntimeError: No CUDA GPUs are available
|
| 173 |
+
Traceback (most recent call last):
|
| 174 |
+
File "<frozen runpy>", line 198, in _run_module_as_main
|
| 175 |
+
File "<frozen runpy>", line 88, in _run_code
|
| 176 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/viper/data/annotate.py", line 147, in <module>
|
| 177 |
+
main()
|
| 178 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/viper/data/annotate.py", line 97, in main
|
| 179 |
+
model = Qwen3VLForConditionalGeneration.from_pretrained(
|
| 180 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 181 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 277, in _wrapper
|
| 182 |
+
return func(*args, **kwargs)
|
| 183 |
+
^^^^^^^^^^^^^^^^^^^^^
|
| 184 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 5048, in from_pretrained
|
| 185 |
+
) = cls._load_pretrained_model(
|
| 186 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 187 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 5432, in _load_pretrained_model
|
| 188 |
+
caching_allocator_warmup(model, expanded_device_map, hf_quantizer)
|
| 189 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 6089, in caching_allocator_warmup
|
| 190 |
+
index = device.index if device.index is not None else torch_accelerator_module.current_device()
|
| 191 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 192 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py", line 940, in current_device
|
| 193 |
+
_lazy_init()
|
| 194 |
+
File "/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py", line 319, in _lazy_init
|
| 195 |
+
torch._C._cuda_init()
|
| 196 |
+
RuntimeError: No CUDA GPUs are available
|
| 197 |
+
srun: error: batch-block5-03408: tasks 1-7: Exited with exit code 1
|
| 198 |
+
srun: Terminating StepId=31601075.0
|
| 199 |
+
|
| 200 |
+
srun: error: batch-block5-03408: task 0: Terminated
|
| 201 |
+
srun: Force Terminated StepId=31601075.0
|
logs/scaleup_1_annotate_31601197.log
ADDED
|
@@ -0,0 +1,365 @@
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|
| 1 |
+
=== clip filtering: reusing 7711 rows ===
|
| 2 |
+
=== 3-axis annotation (Qwen3-VL-4B, 8 ranks) ===
|
| 3 |
+
|
| 4 |
+
|
| 5 |
+
|
| 6 |
+
|
| 7 |
+
|
| 8 |
+
|
| 9 |
+
|
| 10 |
+
|
| 11 |
+
Asked to sample `fps` frames per second but no video metadata was provided which is required when sampling with `fps`. Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results.
|
| 12 |
+
Asked to sample `fps` frames per second but no video metadata was provided which is required when sampling with `fps`. Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results.
|
| 13 |
+
Asked to sample `fps` frames per second but no video metadata was provided which is required when sampling with `fps`. Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results.
|
| 14 |
+
Asked to sample `fps` frames per second but no video metadata was provided which is required when sampling with `fps`. Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results.
|
| 15 |
+
Asked to sample `fps` frames per second but no video metadata was provided which is required when sampling with `fps`. Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results.
|
| 16 |
+
Asked to sample `fps` frames per second but no video metadata was provided which is required when sampling with `fps`. Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results.
|
| 17 |
+
Asked to sample `fps` frames per second but no video metadata was provided which is required when sampling with `fps`. Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results.
|
| 18 |
+
Asked to sample `fps` frames per second but no video metadata was provided which is required when sampling with `fps`. Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results.
|
| 19 |
+
The following generation flags are not valid and may be ignored: ['temperature', 'top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.
|
| 20 |
+
The following generation flags are not valid and may be ignored: ['temperature', 'top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.
|
| 21 |
+
The following generation flags are not valid and may be ignored: ['temperature', 'top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.
|
| 22 |
+
The following generation flags are not valid and may be ignored: ['temperature', 'top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.
|
| 23 |
+
The following generation flags are not valid and may be ignored: ['temperature', 'top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.
|
| 24 |
+
The following generation flags are not valid and may be ignored: ['temperature', 'top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.
|
| 25 |
+
The following generation flags are not valid and may be ignored: ['temperature', 'top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.
|
| 26 |
+
The following generation flags are not valid and may be ignored: ['temperature', 'top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.
|
| 27 |
+
[1] 0/964 ok=1
|
| 28 |
+
[3] 0/964 ok=1
|
| 29 |
+
[5] 0/964 ok=1
|
| 30 |
+
[7] 0/963 ok=1
|
| 31 |
+
[6] 0/964 ok=1
|
| 32 |
+
[4] 0/964 ok=1
|
| 33 |
+
[0] 0/964 ok=1
|
| 34 |
+
[2] 0/964 ok=1
|
| 35 |
+
[1] 25/964 ok=26
|
| 36 |
+
[7] 25/963 ok=26
|
| 37 |
+
[6] 25/964 ok=26
|
| 38 |
+
[3] 25/964 ok=26
|
| 39 |
+
[4] 25/964 ok=26
|
| 40 |
+
[5] 25/964 ok=26
|
| 41 |
+
[2] 25/964 ok=26
|
| 42 |
+
[0] 25/964 ok=26
|
| 43 |
+
[1] 50/964 ok=51
|
| 44 |
+
[7] 50/963 ok=51
|
| 45 |
+
[6] 50/964 ok=51
|
| 46 |
+
[3] 50/964 ok=51
|
| 47 |
+
[5] 50/964 ok=51
|
| 48 |
+
[4] 50/964 ok=51
|
| 49 |
+
[2] 50/964 ok=51
|
| 50 |
+
[0] 50/964 ok=51
|
| 51 |
+
[skip taxonomy] 00002142: {'material': 'plastic_deformable', 'trajectory': 'collision_impact', 'physical_impact': 'collision_impact', 'physics_summary': 'Two vehicles collide head-on, causing immediate plastic deformation and fragmentation of the impact zones, with debris dispersing outward due to the force of impact.'}
|
| 52 |
+
[7] 75/963 ok=76
|
| 53 |
+
[1] 75/964 ok=75
|
| 54 |
+
[4] 75/964 ok=76
|
| 55 |
+
[6] 75/964 ok=76
|
| 56 |
+
[5] 75/964 ok=76
|
| 57 |
+
[3] 75/964 ok=76
|
| 58 |
+
[0] 75/964 ok=76
|
| 59 |
+
[2] 75/964 ok=76
|
| 60 |
+
[skip taxonomy] 00002787: {'material': 'elastic_deformable', 'trajectory': 'swimming', 'physical_impact': 'splash_dispersal', 'physics_summary': 'A person is submerged underwater, swimming with a paddle, causing bubbles and water displacement as they move through the liquid medium.'}
|
| 61 |
+
[7] 100/963 ok=101
|
| 62 |
+
[1] 100/964 ok=100
|
| 63 |
+
[0] 100/964 ok=100
|
| 64 |
+
[4] 100/964 ok=101
|
| 65 |
+
[3] 100/964 ok=101
|
| 66 |
+
[5] 100/964 ok=101
|
| 67 |
+
[2] 100/964 ok=101
|
| 68 |
+
[6] 100/964 ok=101
|
| 69 |
+
[1] 125/964 ok=125
|
| 70 |
+
[7] 125/963 ok=126
|
| 71 |
+
[0] 125/964 ok=125
|
| 72 |
+
[4] 125/964 ok=126
|
| 73 |
+
[5] 125/964 ok=126
|
| 74 |
+
[3] 125/964 ok=126
|
| 75 |
+
[2] 125/964 ok=126
|
| 76 |
+
[6] 125/964 ok=126
|
| 77 |
+
[1] 150/964 ok=150
|
| 78 |
+
[0] 150/964 ok=150
|
| 79 |
+
[7] 150/963 ok=151
|
| 80 |
+
[3] 150/964 ok=151
|
| 81 |
+
[5] 150/964 ok=151
|
| 82 |
+
[4] 150/964 ok=151
|
| 83 |
+
[6] 150/964 ok=151
|
| 84 |
+
[2] 150/964 ok=151
|
| 85 |
+
[0] 175/964 ok=175
|
| 86 |
+
[1] 175/964 ok=175
|
| 87 |
+
[7] 175/963 ok=176
|
| 88 |
+
[5] 175/964 ok=176
|
| 89 |
+
[3] 175/964 ok=176
|
| 90 |
+
[4] 175/964 ok=176
|
| 91 |
+
[2] 175/964 ok=176
|
| 92 |
+
[6] 175/964 ok=176
|
| 93 |
+
[0] 200/964 ok=200
|
| 94 |
+
[1] 200/964 ok=200
|
| 95 |
+
[7] 200/963 ok=201
|
| 96 |
+
[3] 200/964 ok=201
|
| 97 |
+
[5] 200/964 ok=201
|
| 98 |
+
[4] 200/964 ok=201
|
| 99 |
+
[2] 200/964 ok=201
|
| 100 |
+
[skip taxonomy] 00005828: {'material': 'elastic_deformable', 'trajectory': 'compression_squeeze', 'physical_impact': 'compression_squeeze', 'physics_summary': 'A metallic sphere is compressed downward onto a grid of magnetic cubes, causing the cubes to deform and compress under the applied force.'}
|
| 101 |
+
[6] 200/964 ok=201
|
| 102 |
+
[0] 225/964 ok=224
|
| 103 |
+
[1] 225/964 ok=225
|
| 104 |
+
[7] 225/963 ok=226
|
| 105 |
+
[3] 225/964 ok=226
|
| 106 |
+
[5] 225/964 ok=226
|
| 107 |
+
[4] 225/964 ok=226
|
| 108 |
+
[2] 225/964 ok=226
|
| 109 |
+
[6] 225/964 ok=226
|
| 110 |
+
[0] 250/964 ok=249
|
| 111 |
+
[1] 250/964 ok=250
|
| 112 |
+
[7] 250/963 ok=251
|
| 113 |
+
[3] 250/964 ok=251
|
| 114 |
+
[5] 250/964 ok=251
|
| 115 |
+
[2] 250/964 ok=251
|
| 116 |
+
[4] 250/964 ok=251
|
| 117 |
+
[6] 250/964 ok=251
|
| 118 |
+
[0] 275/964 ok=274
|
| 119 |
+
[1] 275/964 ok=275
|
| 120 |
+
[7] 275/963 ok=276
|
| 121 |
+
[5] 275/964 ok=276
|
| 122 |
+
[3] 275/964 ok=276
|
| 123 |
+
[2] 275/964 ok=276
|
| 124 |
+
[4] 275/964 ok=276
|
| 125 |
+
[6] 275/964 ok=276
|
| 126 |
+
[0] 300/964 ok=299
|
| 127 |
+
[7] 300/963 ok=301
|
| 128 |
+
[1] 300/964 ok=300
|
| 129 |
+
[5] 300/964 ok=301
|
| 130 |
+
[3] 300/964 ok=301
|
| 131 |
+
[4] 300/964 ok=301
|
| 132 |
+
[2] 300/964 ok=301
|
| 133 |
+
[6] 300/964 ok=301
|
| 134 |
+
[skip taxonomy] 00008976: {'material': 'plastic_deformable', 'trajectory': 'compression_squeeze', 'physical_impact': 'compression_squeeze', 'physics_summary': 'A rigid cylindrical press applies downward force to a plastic deformable orange sphere, causing it to compress and flatten under the pressure.'}
|
| 135 |
+
[7] 325/963 ok=326
|
| 136 |
+
[0] 325/964 ok=324
|
| 137 |
+
[1] 325/964 ok=325
|
| 138 |
+
[5] 325/964 ok=326
|
| 139 |
+
[3] 325/964 ok=326
|
| 140 |
+
[4] 325/964 ok=326
|
| 141 |
+
[2] 325/964 ok=325
|
| 142 |
+
[6] 325/964 ok=326
|
| 143 |
+
[0] 350/964 ok=349
|
| 144 |
+
[7] 350/963 ok=351
|
| 145 |
+
[1] 350/964 ok=350
|
| 146 |
+
[5] 350/964 ok=351
|
| 147 |
+
[3] 350/964 ok=351
|
| 148 |
+
[4] 350/964 ok=351
|
| 149 |
+
[2] 350/964 ok=350
|
| 150 |
+
[6] 350/964 ok=351
|
| 151 |
+
[7] 375/963 ok=376
|
| 152 |
+
[0] 375/964 ok=374
|
| 153 |
+
[1] 375/964 ok=375
|
| 154 |
+
[5] 375/964 ok=376
|
| 155 |
+
[4] 375/964 ok=376
|
| 156 |
+
[3] 375/964 ok=376
|
| 157 |
+
[2] 375/964 ok=375
|
| 158 |
+
[6] 375/964 ok=376
|
| 159 |
+
[7] 400/963 ok=401
|
| 160 |
+
[0] 400/964 ok=399
|
| 161 |
+
[1] 400/964 ok=400
|
| 162 |
+
[5] 400/964 ok=401
|
| 163 |
+
[4] 400/964 ok=401
|
| 164 |
+
[3] 400/964 ok=401
|
| 165 |
+
[2] 400/964 ok=400
|
| 166 |
+
[6] 400/964 ok=401
|
| 167 |
+
[skip taxonomy] 00012108: {'material': 'brittle', 'trajectory': 'compression_squeeze', 'physical_impact': 'fracture_shatter', 'physics_summary': 'A brittle walnut shell undergoes rapid compression and fracture under the force of a descending hydraulic press, resulting in its immediate shattering and dispersion of fragments.'}
|
| 168 |
+
[7] 425/963 ok=426
|
| 169 |
+
[0] 425/964 ok=424
|
| 170 |
+
[5] 425/964 ok=426
|
| 171 |
+
[1] 425/964 ok=424
|
| 172 |
+
[4] 425/964 ok=426
|
| 173 |
+
[2] 425/964 ok=425
|
| 174 |
+
[3] 425/964 ok=426
|
| 175 |
+
[6] 425/964 ok=426
|
| 176 |
+
[7] 450/963 ok=451
|
| 177 |
+
[skip taxonomy] 00012725: {'material': 'elastic_deformable', 'trajectory': 'swimming', 'physical_impact': 'splash_dispersal', 'physics_summary': 'The dog moves through the water, creating splashes as it swims, with its body deforming slightly to navigate the flow.'}
|
| 178 |
+
[0] 450/964 ok=449
|
| 179 |
+
[5] 450/964 ok=451
|
| 180 |
+
[1] 450/964 ok=449
|
| 181 |
+
[3] 450/964 ok=450
|
| 182 |
+
[4] 450/964 ok=451
|
| 183 |
+
[2] 450/964 ok=450
|
| 184 |
+
[6] 450/964 ok=451
|
| 185 |
+
[7] 475/963 ok=476
|
| 186 |
+
[0] 475/964 ok=474
|
| 187 |
+
[5] 475/964 ok=476
|
| 188 |
+
[1] 475/964 ok=474
|
| 189 |
+
[3] 475/964 ok=475
|
| 190 |
+
[4] 475/964 ok=476
|
| 191 |
+
[2] 475/964 ok=475
|
| 192 |
+
[6] 475/964 ok=476
|
| 193 |
+
[7] 500/963 ok=501
|
| 194 |
+
[0] 500/964 ok=499
|
| 195 |
+
[1] 500/964 ok=499
|
| 196 |
+
[5] 500/964 ok=501
|
| 197 |
+
[3] 500/964 ok=500
|
| 198 |
+
[4] 500/964 ok=501
|
| 199 |
+
[2] 500/964 ok=500
|
| 200 |
+
[skip taxonomy] 00014456: {'material': 'elastic_deformable', 'trajectory': 'collision_impact', 'physical_impact': 'fracture_shatter', 'physics_summary': "A blue car collides with a barrier, causing its structure to fracture and shatter as the impact force is absorbed and distributed through the vehicle's materials."}
|
| 201 |
+
[6] 500/964 ok=501
|
| 202 |
+
[7] 525/963 ok=526
|
| 203 |
+
[skip taxonomy] 00014782: {'material': 'plastic_deformable', 'trajectory': 'compression_squeeze', 'physical_impact': 'compression_squeeze', 'physics_summary': 'A rigid metallic skull is compressed under a heavy press, causing it to deform plastically as the force is applied from above.'}
|
| 204 |
+
[0] 525/964 ok=524
|
| 205 |
+
[1] 525/964 ok=524
|
| 206 |
+
[5] 525/964 ok=525
|
| 207 |
+
[4] 525/964 ok=525
|
| 208 |
+
[3] 525/964 ok=525
|
| 209 |
+
[2] 525/964 ok=525
|
| 210 |
+
[6] 525/964 ok=526
|
| 211 |
+
[7] 550/963 ok=551
|
| 212 |
+
[0] 550/964 ok=549
|
| 213 |
+
[5] 550/964 ok=550
|
| 214 |
+
[1] 550/964 ok=549
|
| 215 |
+
[4] 550/964 ok=550
|
| 216 |
+
[3] 550/964 ok=550
|
| 217 |
+
[2] 550/964 ok=550
|
| 218 |
+
[6] 550/964 ok=551
|
| 219 |
+
[7] 575/963 ok=576
|
| 220 |
+
[0] 575/964 ok=574
|
| 221 |
+
[1] 575/964 ok=574
|
| 222 |
+
[5] 575/964 ok=575
|
| 223 |
+
[3] 575/964 ok=575
|
| 224 |
+
[4] 575/964 ok=575
|
| 225 |
+
[2] 575/964 ok=575
|
| 226 |
+
[6] 575/964 ok=576
|
| 227 |
+
[7] 600/963 ok=601
|
| 228 |
+
[0] 600/964 ok=599
|
| 229 |
+
[1] 600/964 ok=599
|
| 230 |
+
[5] 600/964 ok=600
|
| 231 |
+
[3] 600/964 ok=600
|
| 232 |
+
[4] 600/964 ok=600
|
| 233 |
+
[2] 600/964 ok=600
|
| 234 |
+
[6] 600/964 ok=601
|
| 235 |
+
[7] 625/963 ok=626
|
| 236 |
+
[0] 625/964 ok=624
|
| 237 |
+
[skip taxonomy] 00017577: {'material': 'elastic_deformable', 'trajectory': 'bounce_rebound', 'physical_impact': 'bounce_rebound', 'physics_summary': 'A tennis ball bounces upward after hitting a surface, exhibiting elastic deformation and rebounding motion due to its elastic properties.'}
|
| 238 |
+
[1] 625/964 ok=624
|
| 239 |
+
[5] 625/964 ok=625
|
| 240 |
+
[skip taxonomy] 00017778: {'material': 'viscous_liquid', 'trajectory': 'splash_dispersal', 'physical_impact': 'splash_dispersal', 'physics_summary': 'A droplet impacts a viscous liquid surface, causing a splash and dispersal of smaller droplets as the liquid deforms and flows outward.'}
|
| 241 |
+
[4] 625/964 ok=624
|
| 242 |
+
[2] 625/964 ok=625
|
| 243 |
+
[6] 625/964 ok=626
|
| 244 |
+
[skip taxonomy] 00018404: {'material': 'plastic_deformable', 'trajectory': 'collision_impact', 'physical_impact': 'collision_impact', 'physics_summary': 'Two vehicles collide head-on, causing immediate plastic deformation and shattering of materials, with fragments dispersing due to the impact force.'}
|
| 245 |
+
[7] 650/963 ok=651
|
| 246 |
+
[0] 650/964 ok=648
|
| 247 |
+
[5] 650/964 ok=650
|
| 248 |
+
[1] 650/964 ok=649
|
| 249 |
+
[3] 650/964 ok=649
|
| 250 |
+
[2] 650/964 ok=650
|
| 251 |
+
[4] 650/964 ok=649
|
| 252 |
+
[6] 650/964 ok=651
|
| 253 |
+
[7] 675/963 ok=676
|
| 254 |
+
[0] 675/964 ok=673
|
| 255 |
+
[1] 675/964 ok=674
|
| 256 |
+
[5] 675/964 ok=675
|
| 257 |
+
[3] 675/964 ok=674
|
| 258 |
+
[2] 675/964 ok=675
|
| 259 |
+
[4] 675/964 ok=674
|
| 260 |
+
[6] 675/964 ok=676
|
| 261 |
+
[7] 700/963 ok=701
|
| 262 |
+
[0] 700/964 ok=698
|
| 263 |
+
[1] 700/964 ok=699
|
| 264 |
+
[5] 700/964 ok=700
|
| 265 |
+
[3] 700/964 ok=699
|
| 266 |
+
[2] 700/964 ok=700
|
| 267 |
+
[4] 700/964 ok=699
|
| 268 |
+
[6] 700/964 ok=701
|
| 269 |
+
[0] 725/964 ok=723
|
| 270 |
+
[7] 725/963 ok=726
|
| 271 |
+
[1] 725/964 ok=724
|
| 272 |
+
[5] 725/964 ok=725
|
| 273 |
+
[3] 725/964 ok=724
|
| 274 |
+
[4] 725/964 ok=724
|
| 275 |
+
[2] 725/964 ok=725
|
| 276 |
+
[skip taxonomy] 00020773: {'material': 'brittle', 'trajectory': 'compression_squeeze', 'physical_impact': 'fracture_shatter', 'physics_summary': 'A rigid metallic press applies compressive force to a brittle rock, causing it to fracture and shatter into smaller fragments under high pressure.'}
|
| 277 |
+
[6] 725/964 ok=726
|
| 278 |
+
[0] 750/964 ok=748
|
| 279 |
+
[7] 750/963 ok=751
|
| 280 |
+
[5] 750/964 ok=750
|
| 281 |
+
[1] 750/964 ok=749
|
| 282 |
+
[skip taxonomy] 00021349: {'material': 'elastic_deformable', 'trajectory': 'swimming', 'physical_impact': 'splash_dispersal', 'physics_summary': 'The object moves through water with splashes dispersing around it, indicating elastic deformation and fluid resistance as it propels forward.'}
|
| 283 |
+
[3] 750/964 ok=747
|
| 284 |
+
[2] 750/964 ok=750
|
| 285 |
+
[4] 750/964 ok=749
|
| 286 |
+
[6] 750/964 ok=751
|
| 287 |
+
[skip taxonomy] 00021639: {'material': 'viscous_liquid', 'trajectory': 'compression_squeeze', 'physical_impact': 'tearing_rupture', 'physics_summary': 'A viscous liquid inside a transparent container is subjected to compression from above, causing it to rupture and disperse outward as the container deforms under pressure.'}
|
| 288 |
+
[skip taxonomy] 00021749: {'material': 'elastic_deformable', 'trajectory': 'swimming', 'physical_impact': 'splash_dispersal', 'physics_summary': 'The bird submerges into the water, creating a splash as it moves through the liquid medium, demonstrating elastic deformation of its body and interaction with the water surface.'}
|
| 289 |
+
[0] 775/964 ok=773
|
| 290 |
+
[7] 775/963 ok=776
|
| 291 |
+
[5] 775/964 ok=775
|
| 292 |
+
[1] 775/964 ok=774
|
| 293 |
+
[3] 775/964 ok=772
|
| 294 |
+
[2] 775/964 ok=774
|
| 295 |
+
[4] 775/964 ok=773
|
| 296 |
+
[6] 775/964 ok=776
|
| 297 |
+
[0] 800/964 ok=798
|
| 298 |
+
[7] 800/963 ok=801
|
| 299 |
+
[5] 800/964 ok=800
|
| 300 |
+
[1] 800/964 ok=799
|
| 301 |
+
[3] 800/964 ok=797
|
| 302 |
+
[2] 800/964 ok=799
|
| 303 |
+
[4] 800/964 ok=798
|
| 304 |
+
[6] 800/964 ok=801
|
| 305 |
+
[0] 825/964 ok=823
|
| 306 |
+
[7] 825/963 ok=826
|
| 307 |
+
[5] 825/964 ok=825
|
| 308 |
+
[skip taxonomy] 00023609: {'material': 'plastic_deformable', 'trajectory': 'collision_impact', 'physical_impact': 'collision_impact', 'physics_summary': 'Two vehicles collide head-on, causing immediate plastic deformation and fragmentation of the impact zones, with debris dispersing outward due to the force of impact.'}
|
| 309 |
+
[1] 825/964 ok=824
|
| 310 |
+
[3] 825/964 ok=822
|
| 311 |
+
[2] 825/964 ok=824
|
| 312 |
+
[4] 825/964 ok=823
|
| 313 |
+
[6] 825/964 ok=826
|
| 314 |
+
[0] 850/964 ok=848
|
| 315 |
+
[7] 850/963 ok=850
|
| 316 |
+
[5] 850/964 ok=850
|
| 317 |
+
[1] 850/964 ok=849
|
| 318 |
+
[3] 850/964 ok=847
|
| 319 |
+
[2] 850/964 ok=849
|
| 320 |
+
[4] 850/964 ok=848
|
| 321 |
+
[6] 850/964 ok=851
|
| 322 |
+
[skip taxonomy] 00024444: {'material': 'elastic_deformable', 'trajectory': 'swimming', 'physical_impact': 'no_impact', 'physics_summary': 'A person swims underwater, moving through the water with fluid motion, their body deforming slightly as they propel themselves forward without significant impact or resistance.'}
|
| 323 |
+
[0] 875/964 ok=873
|
| 324 |
+
[7] 875/963 ok=875
|
| 325 |
+
[5] 875/964 ok=875
|
| 326 |
+
[1] 875/964 ok=874
|
| 327 |
+
[3] 875/964 ok=872
|
| 328 |
+
[2] 875/964 ok=873
|
| 329 |
+
[4] 875/964 ok=873
|
| 330 |
+
[6] 875/964 ok=876
|
| 331 |
+
[skip taxonomy] 00025187: {'material': 'solid', 'trajectory': 'in_place_deformation', 'physical_impact': 'melting_phase_change', 'physics_summary': "Ice cubes in a pan are undergoing a phase change from solid to liquid as they melt due to heat transfer from the pan's surface."}
|
| 332 |
+
[0] 900/964 ok=898
|
| 333 |
+
[7] 900/963 ok=900
|
| 334 |
+
[5] 900/964 ok=900
|
| 335 |
+
[1] 900/964 ok=899
|
| 336 |
+
[3] 900/964 ok=896
|
| 337 |
+
[2] 900/964 ok=898
|
| 338 |
+
[4] 900/964 ok=898
|
| 339 |
+
[6] 900/964 ok=901
|
| 340 |
+
[0] 925/964 ok=923
|
| 341 |
+
[7] 925/963 ok=925
|
| 342 |
+
[5] 925/964 ok=925
|
| 343 |
+
[1] 925/964 ok=924
|
| 344 |
+
[3] 925/964 ok=921
|
| 345 |
+
[2] 925/964 ok=923
|
| 346 |
+
[4] 925/964 ok=923
|
| 347 |
+
[6] 925/964 ok=926
|
| 348 |
+
[0] 950/964 ok=948
|
| 349 |
+
[7] 950/963 ok=950
|
| 350 |
+
[5] 950/964 ok=950
|
| 351 |
+
[1] 950/964 ok=949
|
| 352 |
+
[3] 950/964 ok=946
|
| 353 |
+
[2] 950/964 ok=948
|
| 354 |
+
[0] DONE ok=961/964 -> data/annotated.jsonl.0
|
| 355 |
+
[7] DONE ok=962/963 -> data/annotated.jsonl.7
|
| 356 |
+
[4] 950/964 ok=948
|
| 357 |
+
[6] 950/964 ok=951
|
| 358 |
+
[5] DONE ok=963/964 -> data/annotated.jsonl.5
|
| 359 |
+
[1] DONE ok=962/964 -> data/annotated.jsonl.1
|
| 360 |
+
[3] DONE ok=959/964 -> data/annotated.jsonl.3
|
| 361 |
+
[2] DONE ok=961/964 -> data/annotated.jsonl.2
|
| 362 |
+
[4] DONE ok=961/964 -> data/annotated.jsonl.4
|
| 363 |
+
[6] DONE ok=964/964 -> data/annotated.jsonl.6
|
| 364 |
+
filtered_clips: 7711
|
| 365 |
+
annotated: 7693
|
logs/scaleup_2_pairs_31601200.log
ADDED
|
@@ -0,0 +1,650 @@
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|
|
|
| 1 |
+
=== candidate pairs (bucketing only, for the record) ===
|
| 2 |
+
candidate pairs: 15608
|
| 3 |
+
{
|
| 4 |
+
"('splash_dispersal', 'free_fall')": 400,
|
| 5 |
+
"('no_impact', 'in_place_deformation')": 400,
|
| 6 |
+
"('splash_dispersal', 'sliding')": 400,
|
| 7 |
+
"('no_impact', 'flowing_pouring')": 400,
|
| 8 |
+
"('splash_dispersal', 'flowing_pouring')": 400,
|
| 9 |
+
"('no_impact', 'expanding_dispersing')": 400,
|
| 10 |
+
"('compression_squeeze', 'in_place_deformation')": 400,
|
| 11 |
+
"('bounce_rebound', 'rolling')": 400,
|
| 12 |
+
"('splash_dispersal', 'expanding_dispersing')": 400,
|
| 13 |
+
"('melting_phase_change', 'in_place_deformation')": 400,
|
| 14 |
+
"('compression_squeeze', 'sliding')": 400,
|
| 15 |
+
"('no_impact', 'rotating_spinning')": 400,
|
| 16 |
+
"('compression_squeeze', 'expanding_dispersing')": 45,
|
| 17 |
+
"('no_impact', 'sliding')": 400,
|
| 18 |
+
"('fracture_shatter', 'sliding')": 400,
|
| 19 |
+
"('bounce_rebound', 'oscillating_vibrating')": 45,
|
| 20 |
+
"('no_impact', 'projectile_arc')": 400,
|
| 21 |
+
"('bounce_rebound', 'projectile_arc')": 400,
|
| 22 |
+
"('no_impact', 'rolling')": 400,
|
| 23 |
+
"('bounce_rebound', 'sliding')": 171,
|
| 24 |
+
"('compression_squeeze', 'free_fall')": 300,
|
| 25 |
+
"('no_impact', 'free_fall')": 400,
|
| 26 |
+
"('collision_impact', 'sliding')": 400,
|
| 27 |
+
"('fracture_shatter', 'rotating_spinning')": 36,
|
| 28 |
+
"('no_impact', 'swinging_pendulum')": 400,
|
| 29 |
+
"('bounce_rebound', 'free_fall')": 400,
|
| 30 |
+
"('melting_phase_change', 'expanding_dispersing')": 136,
|
| 31 |
+
"('collision_impact', 'free_fall')": 400,
|
| 32 |
+
"('no_impact', 'oscillating_vibrating')": 400,
|
| 33 |
+
"('melting_phase_change', 'sliding')": 231,
|
| 34 |
+
"('fracture_shatter', 'free_fall')": 400,
|
| 35 |
+
"('fracture_shatter', 'expanding_dispersing')": 400,
|
| 36 |
+
"('crumbling', 'sliding')": 28,
|
| 37 |
+
"('collision_impact', 'swinging_pendulum')": 120,
|
| 38 |
+
"('collision_impact', 'rolling')": 400,
|
| 39 |
+
"('bounce_rebound', 'rotating_spinning')": 78,
|
| 40 |
+
"('compression_squeeze', 'rolling')": 400,
|
| 41 |
+
"('collision_impact', 'flowing_pouring')": 136,
|
| 42 |
+
"('splash_dispersal', 'projectile_arc')": 400,
|
| 43 |
+
"('fracture_shatter', 'in_place_deformation')": 91,
|
| 44 |
+
"('tearing_rupture', 'sliding')": 400,
|
| 45 |
+
"('tearing_rupture', 'expanding_dispersing')": 45,
|
| 46 |
+
"('crumbling', 'in_place_deformation')": 105,
|
| 47 |
+
"('melting_phase_change', 'flowing_pouring')": 400,
|
| 48 |
+
"('fracture_shatter', 'rolling')": 36,
|
| 49 |
+
"('compression_squeeze', 'oscillating_vibrating')": 36,
|
| 50 |
+
"('splash_dispersal', 'swinging_pendulum')": 28,
|
| 51 |
+
"('tearing_rupture', 'rotating_spinning')": 1,
|
| 52 |
+
"('tearing_rupture', 'in_place_deformation')": 400,
|
| 53 |
+
"('melting_phase_change', 'free_fall')": 91,
|
| 54 |
+
"('tearing_rupture', 'free_fall')": 21,
|
| 55 |
+
"('fracture_shatter', 'flowing_pouring')": 28,
|
| 56 |
+
"('splash_dispersal', 'rolling')": 105,
|
| 57 |
+
"('compression_squeeze', 'rotating_spinning')": 400,
|
| 58 |
+
"('collision_impact', 'projectile_arc')": 400,
|
| 59 |
+
"('crumbling', 'free_fall')": 21,
|
| 60 |
+
"('compression_squeeze', 'flowing_pouring')": 136,
|
| 61 |
+
"('splash_dispersal', 'rotating_spinning')": 36,
|
| 62 |
+
"('collision_impact', 'rotating_spinning')": 10,
|
| 63 |
+
"('fracture_shatter', 'projectile_arc')": 91,
|
| 64 |
+
"('bounce_rebound', 'flowing_pouring')": 6,
|
| 65 |
+
"('collision_impact', 'oscillating_vibrating')": 55,
|
| 66 |
+
"('crumbling', 'expanding_dispersing')": 21,
|
| 67 |
+
"('collision_impact', 'in_place_deformation')": 28,
|
| 68 |
+
"('bounce_rebound', 'swinging_pendulum')": 10,
|
| 69 |
+
"('melting_phase_change', 'projectile_arc')": 10,
|
| 70 |
+
"('dent_deformation', 'sliding')": 55,
|
| 71 |
+
"('splash_dispersal', 'in_place_deformation')": 6,
|
| 72 |
+
"('crumbling', 'rotating_spinning')": 3,
|
| 73 |
+
"('tearing_rupture', 'rolling')": 1,
|
| 74 |
+
"('bounce_rebound', 'expanding_dispersing')": 6
|
| 75 |
+
}
|
| 76 |
+
wrote 15608 unjudged pairs -> data/pairs_candidates.jsonl
|
| 77 |
+
=== transferability judging (Qwen3-VL-32B, 4 ranks x 2 GPUs) ===
|
| 78 |
+
candidate pairs: 15608
|
| 79 |
+
{
|
| 80 |
+
"('splash_dispersal', 'free_fall')": 400,
|
| 81 |
+
"('no_impact', 'in_place_deformation')": 400,
|
| 82 |
+
"('splash_dispersal', 'sliding')": 400,
|
| 83 |
+
"('no_impact', 'flowing_pouring')": 400,
|
| 84 |
+
"('splash_dispersal', 'flowing_pouring')": 400,
|
| 85 |
+
"('no_impact', 'expanding_dispersing')": 400,
|
| 86 |
+
"('compression_squeeze', 'in_place_deformation')": 400,
|
| 87 |
+
"('bounce_rebound', 'rolling')": 400,
|
| 88 |
+
"('splash_dispersal', 'expanding_dispersing')": 400,
|
| 89 |
+
"('melting_phase_change', 'in_place_deformation')": 400,
|
| 90 |
+
"('compression_squeeze', 'sliding')": 400,
|
| 91 |
+
"('no_impact', 'rotating_spinning')": 400,
|
| 92 |
+
"('compression_squeeze', 'expanding_dispersing')": 45,
|
| 93 |
+
"('no_impact', 'sliding')": 400,
|
| 94 |
+
"('fracture_shatter', 'sliding')": 400,
|
| 95 |
+
"('bounce_rebound', 'oscillating_vibrating')": 45,
|
| 96 |
+
"('no_impact', 'projectile_arc')": 400,
|
| 97 |
+
"('bounce_rebound', 'projectile_arc')": 400,
|
| 98 |
+
"('no_impact', 'rolling')": 400,
|
| 99 |
+
"('bounce_rebound', 'sliding')": 171,
|
| 100 |
+
"('compression_squeeze', 'free_fall')": 300,
|
| 101 |
+
"('no_impact', 'free_fall')": 400,
|
| 102 |
+
"('collision_impact', 'sliding')": 400,
|
| 103 |
+
"('fracture_shatter', 'rotating_spinning')": 36,
|
| 104 |
+
"('no_impact', 'swinging_pendulum')": 400,
|
| 105 |
+
"('bounce_rebound', 'free_fall')": 400,
|
| 106 |
+
"('melting_phase_change', 'expanding_dispersing')": 136,
|
| 107 |
+
"('collision_impact', 'free_fall')": 400,
|
| 108 |
+
"('no_impact', 'oscillating_vibrating')": 400,
|
| 109 |
+
"('melting_phase_change', 'sliding')": 231,
|
| 110 |
+
"('fracture_shatter', 'free_fall')": 400,
|
| 111 |
+
"('fracture_shatter', 'expanding_dispersing')": 400,
|
| 112 |
+
"('crumbling', 'sliding')": 28,
|
| 113 |
+
"('collision_impact', 'swinging_pendulum')": 120,
|
| 114 |
+
"('collision_impact', 'rolling')": 400,
|
| 115 |
+
"('bounce_rebound', 'rotating_spinning')": 78,
|
| 116 |
+
"('compression_squeeze', 'rolling')": 400,
|
| 117 |
+
"('collision_impact', 'flowing_pouring')": 136,
|
| 118 |
+
"('splash_dispersal', 'projectile_arc')": 400,
|
| 119 |
+
"('fracture_shatter', 'in_place_deformation')": 91,
|
| 120 |
+
"('tearing_rupture', 'sliding')": 400,
|
| 121 |
+
"('tearing_rupture', 'expanding_dispersing')": 45,
|
| 122 |
+
"('crumbling', 'in_place_deformation')": 105,
|
| 123 |
+
"('melting_phase_change', 'flowing_pouring')": 400,
|
| 124 |
+
"('fracture_shatter', 'rolling')": 36,
|
| 125 |
+
"('compression_squeeze', 'oscillating_vibrating')": 36,
|
| 126 |
+
"('splash_dispersal', 'swinging_pendulum')": 28,
|
| 127 |
+
"('tearing_rupture', 'rotating_spinning')": 1,
|
| 128 |
+
"('tearing_rupture', 'in_place_deformation')": 400,
|
| 129 |
+
"('melting_phase_change', 'free_fall')": 91,
|
| 130 |
+
"('tearing_rupture', 'free_fall')": 21,
|
| 131 |
+
"('fracture_shatter', 'flowing_pouring')": 28,
|
| 132 |
+
"('splash_dispersal', 'rolling')": 105,
|
| 133 |
+
"('compression_squeeze', 'rotating_spinning')": 400,
|
| 134 |
+
"('collision_impact', 'projectile_arc')": 400,
|
| 135 |
+
"('crumbling', 'free_fall')": 21,
|
| 136 |
+
"('compression_squeeze', 'flowing_pouring')": 136,
|
| 137 |
+
"('splash_dispersal', 'rotating_spinning')": 36,
|
| 138 |
+
"('collision_impact', 'rotating_spinning')": 10,
|
| 139 |
+
"('fracture_shatter', 'projectile_arc')": 91,
|
| 140 |
+
"('bounce_rebound', 'flowing_pouring')": 6,
|
| 141 |
+
"('collision_impact', 'oscillating_vibrating')": 55,
|
| 142 |
+
"('crumbling', 'expanding_dispersing')": 21,
|
| 143 |
+
"('collision_impact', 'in_place_deformation')": 28,
|
| 144 |
+
"('bounce_rebound', 'swinging_pendulum')": 10,
|
| 145 |
+
"('melting_phase_change', 'projectile_arc')": 10,
|
| 146 |
+
"('dent_deformation', 'sliding')": 55,
|
| 147 |
+
"('splash_dispersal', 'in_place_deformation')": 6,
|
| 148 |
+
"('crumbling', 'rotating_spinning')": 3,
|
| 149 |
+
"('tearing_rupture', 'rolling')": 1,
|
| 150 |
+
"('bounce_rebound', 'expanding_dispersing')": 6
|
| 151 |
+
}
|
| 152 |
+
candidate pairs: 15608
|
| 153 |
+
candidate pairs: 15608
|
| 154 |
+
candidate pairs: 15608
|
| 155 |
+
|
| 156 |
+
�██████▌ | 12/14 [04:49<00:46, 23.04s/it]
|
| 157 |
+
�██████▌ | 12/14 [04:49<00:46, 23.05s/it]
|
| 158 |
+
�██████▌ | 12/14 [04:49<00:46, 23.05s/it]
|
| 159 |
+
Asked to sample `fps` frames per second but no video metadata was provided which is required when sampling with `fps`. Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results.
|
| 160 |
+
The following generation flags are not valid and may be ignored: ['temperature', 'top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.
|
| 161 |
+
Asked to sample `fps` frames per second but no video metadata was provided which is required when sampling with `fps`. Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results.
|
| 162 |
+
Asked to sample `fps` frames per second but no video metadata was provided which is required when sampling with `fps`. Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results.
|
| 163 |
+
Asked to sample `fps` frames per second but no video metadata was provided which is required when sampling with `fps`. Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results.
|
| 164 |
+
The following generation flags are not valid and may be ignored: ['temperature', 'top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.
|
| 165 |
+
The following generation flags are not valid and may be ignored: ['temperature', 'top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.
|
| 166 |
+
The following generation flags are not valid and may be ignored: ['temperature', 'top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.
|
| 167 |
+
0/3902 kept=0
|
| 168 |
+
0/3902 kept=0
|
| 169 |
+
0/3902 kept=0
|
| 170 |
+
0/3902 kept=0
|
| 171 |
+
20/3902 kept=2
|
| 172 |
+
20/3902 kept=3
|
| 173 |
+
20/3902 kept=1
|
| 174 |
+
20/3902 kept=1
|
| 175 |
+
40/3902 kept=4
|
| 176 |
+
40/3902 kept=5
|
| 177 |
+
40/3902 kept=4
|
| 178 |
+
40/3902 kept=2
|
| 179 |
+
60/3902 kept=4
|
| 180 |
+
60/3902 kept=4
|
| 181 |
+
60/3902 kept=4
|
| 182 |
+
60/3902 kept=5
|
| 183 |
+
80/3902 kept=5
|
| 184 |
+
80/3902 kept=6
|
| 185 |
+
80/3902 kept=9
|
| 186 |
+
80/3902 kept=6
|
| 187 |
+
100/3902 kept=9
|
| 188 |
+
100/3902 kept=11
|
| 189 |
+
100/3902 kept=11
|
| 190 |
+
100/3902 kept=7
|
| 191 |
+
120/3902 kept=10
|
| 192 |
+
120/3902 kept=11
|
| 193 |
+
120/3902 kept=13
|
| 194 |
+
120/3902 kept=9
|
| 195 |
+
140/3902 kept=12
|
| 196 |
+
140/3902 kept=12
|
| 197 |
+
140/3902 kept=16
|
| 198 |
+
140/3902 kept=10
|
| 199 |
+
160/3902 kept=15
|
| 200 |
+
160/3902 kept=16
|
| 201 |
+
160/3902 kept=13
|
| 202 |
+
160/3902 kept=21
|
| 203 |
+
180/3902 kept=17
|
| 204 |
+
180/3902 kept=17
|
| 205 |
+
180/3902 kept=16
|
| 206 |
+
180/3902 kept=22
|
| 207 |
+
200/3902 kept=18
|
| 208 |
+
200/3902 kept=18
|
| 209 |
+
200/3902 kept=18
|
| 210 |
+
200/3902 kept=26
|
| 211 |
+
220/3902 kept=19
|
| 212 |
+
220/3902 kept=21
|
| 213 |
+
220/3902 kept=19
|
| 214 |
+
220/3902 kept=28
|
| 215 |
+
240/3902 kept=22
|
| 216 |
+
240/3902 kept=23
|
| 217 |
+
240/3902 kept=20
|
| 218 |
+
240/3902 kept=29
|
| 219 |
+
260/3902 kept=22
|
| 220 |
+
260/3902 kept=22
|
| 221 |
+
260/3902 kept=23
|
| 222 |
+
260/3902 kept=33
|
| 223 |
+
280/3902 kept=22
|
| 224 |
+
280/3902 kept=24
|
| 225 |
+
280/3902 kept=24
|
| 226 |
+
280/3902 kept=35
|
| 227 |
+
300/3902 kept=25
|
| 228 |
+
300/3902 kept=29
|
| 229 |
+
300/3902 kept=27
|
| 230 |
+
300/3902 kept=37
|
| 231 |
+
320/3902 kept=28
|
| 232 |
+
320/3902 kept=30
|
| 233 |
+
320/3902 kept=28
|
| 234 |
+
320/3902 kept=39
|
| 235 |
+
340/3902 kept=30
|
| 236 |
+
340/3902 kept=28
|
| 237 |
+
340/3902 kept=33
|
| 238 |
+
340/3902 kept=44
|
| 239 |
+
360/3902 kept=31
|
| 240 |
+
360/3902 kept=30
|
| 241 |
+
360/3902 kept=33
|
| 242 |
+
360/3902 kept=47
|
| 243 |
+
380/3902 kept=34
|
| 244 |
+
380/3902 kept=34
|
| 245 |
+
380/3902 kept=35
|
| 246 |
+
380/3902 kept=48
|
| 247 |
+
400/3902 kept=35
|
| 248 |
+
400/3902 kept=36
|
| 249 |
+
400/3902 kept=35
|
| 250 |
+
400/3902 kept=49
|
| 251 |
+
420/3902 kept=37
|
| 252 |
+
420/3902 kept=37
|
| 253 |
+
420/3902 kept=37
|
| 254 |
+
420/3902 kept=52
|
| 255 |
+
440/3902 kept=39
|
| 256 |
+
440/3902 kept=40
|
| 257 |
+
440/3902 kept=37
|
| 258 |
+
440/3902 kept=53
|
| 259 |
+
460/3902 kept=43
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1480/3902 kept=125
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1500/3902 kept=154
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1500/3902 kept=158
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1500/3902 kept=126
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1500/3902 kept=154
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1540/3902 kept=131
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1580/3902 kept=164
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1580/3902 kept=133
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1600/3902 kept=163
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1600/3902 kept=136
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1600/3902 kept=165
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1620/3902 kept=167
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1620/3902 kept=137
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1700/3902 kept=146
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|
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1720/3902 kept=182
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1720/3902 kept=180
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1740/3902 kept=182
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1740/3902 kept=149
|
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1740/3902 kept=177
|
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1780/3902 kept=187
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1780/3902 kept=153
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1780/3902 kept=182
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1800/3902 kept=188
|
| 528 |
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1800/3902 kept=188
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| 529 |
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1800/3902 kept=156
|
| 530 |
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1800/3902 kept=184
|
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1820/3902 kept=191
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| 532 |
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1820/3902 kept=188
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| 533 |
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1820/3902 kept=157
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| 534 |
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1820/3902 kept=186
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1840/3902 kept=193
|
| 536 |
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1840/3902 kept=191
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| 537 |
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1840/3902 kept=159
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1840/3902 kept=189
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|
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1880/3902 kept=197
|
| 544 |
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1880/3902 kept=194
|
| 545 |
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1880/3902 kept=165
|
| 546 |
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1880/3902 kept=192
|
| 547 |
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1900/3902 kept=197
|
| 548 |
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1900/3902 kept=195
|
| 549 |
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1900/3902 kept=170
|
| 550 |
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1900/3902 kept=194
|
| 551 |
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1920/3902 kept=199
|
| 552 |
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1920/3902 kept=197
|
| 553 |
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1920/3902 kept=173
|
| 554 |
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1920/3902 kept=197
|
| 555 |
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1940/3902 kept=200
|
| 556 |
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1940/3902 kept=199
|
| 557 |
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1940/3902 kept=175
|
| 558 |
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1940/3902 kept=199
|
| 559 |
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1960/3902 kept=201
|
| 560 |
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1960/3902 kept=200
|
| 561 |
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1960/3902 kept=177
|
| 562 |
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1960/3902 kept=200
|
| 563 |
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1980/3902 kept=203
|
| 564 |
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1980/3902 kept=200
|
| 565 |
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1980/3902 kept=179
|
| 566 |
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1980/3902 kept=204
|
| 567 |
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2000/3902 kept=206
|
| 568 |
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2000/3902 kept=204
|
| 569 |
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2000/3902 kept=181
|
| 570 |
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2000/3902 kept=205
|
| 571 |
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2020/3902 kept=207
|
| 572 |
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2020/3902 kept=205
|
| 573 |
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2020/3902 kept=183
|
| 574 |
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2020/3902 kept=205
|
| 575 |
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2040/3902 kept=208
|
| 576 |
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2040/3902 kept=207
|
| 577 |
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2040/3902 kept=186
|
| 578 |
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|
| 579 |
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|
| 580 |
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2060/3902 kept=208
|
| 581 |
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|
| 582 |
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|
| 583 |
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|
| 584 |
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2080/3902 kept=209
|
| 585 |
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2080/3902 kept=189
|
| 586 |
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2080/3902 kept=209
|
| 587 |
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2100/3902 kept=210
|
| 588 |
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2100/3902 kept=211
|
| 589 |
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2100/3902 kept=190
|
| 590 |
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2100/3902 kept=214
|
| 591 |
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2120/3902 kept=214
|
| 592 |
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2120/3902 kept=212
|
| 593 |
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2120/3902 kept=190
|
| 594 |
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2120/3902 kept=216
|
| 595 |
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2140/3902 kept=215
|
| 596 |
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2140/3902 kept=215
|
| 597 |
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2140/3902 kept=190
|
| 598 |
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2140/3902 kept=217
|
| 599 |
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2160/3902 kept=218
|
| 600 |
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2160/3902 kept=216
|
| 601 |
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2160/3902 kept=193
|
| 602 |
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2160/3902 kept=220
|
| 603 |
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2180/3902 kept=218
|
| 604 |
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2180/3902 kept=216
|
| 605 |
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2180/3902 kept=195
|
| 606 |
+
2180/3902 kept=222
|
| 607 |
+
2200/3902 kept=219
|
| 608 |
+
2200/3902 kept=218
|
| 609 |
+
2200/3902 kept=198
|
| 610 |
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2200/3902 kept=225
|
| 611 |
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2220/3902 kept=220
|
| 612 |
+
2220/3902 kept=221
|
| 613 |
+
2220/3902 kept=198
|
| 614 |
+
2220/3902 kept=227
|
| 615 |
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2240/3902 kept=223
|
| 616 |
+
2240/3902 kept=222
|
| 617 |
+
2240/3902 kept=199
|
| 618 |
+
2240/3902 kept=230
|
| 619 |
+
2260/3902 kept=227
|
| 620 |
+
2260/3902 kept=224
|
| 621 |
+
2260/3902 kept=199
|
| 622 |
+
2260/3902 kept=233
|
| 623 |
+
2280/3902 kept=230
|
| 624 |
+
2280/3902 kept=226
|
| 625 |
+
2280/3902 kept=200
|
| 626 |
+
2280/3902 kept=234
|
| 627 |
+
2300/3902 kept=230
|
| 628 |
+
2300/3902 kept=229
|
| 629 |
+
2300/3902 kept=202
|
| 630 |
+
2300/3902 kept=236
|
| 631 |
+
2320/3902 kept=235
|
| 632 |
+
2320/3902 kept=229
|
| 633 |
+
2320/3902 kept=205
|
| 634 |
+
2320/3902 kept=236
|
| 635 |
+
2340/3902 kept=236
|
| 636 |
+
2340/3902 kept=231
|
| 637 |
+
2340/3902 kept=208
|
| 638 |
+
2340/3902 kept=237
|
| 639 |
+
2360/3902 kept=237
|
| 640 |
+
2360/3902 kept=233
|
| 641 |
+
2360/3902 kept=210
|
| 642 |
+
2360/3902 kept=243
|
| 643 |
+
2380/3902 kept=238
|
| 644 |
+
2380/3902 kept=234
|
| 645 |
+
2380/3902 kept=214
|
| 646 |
+
2380/3902 kept=245
|
| 647 |
+
2400/3902 kept=239
|
| 648 |
+
Aug 06 17:30:45.185423 1679552 slurmstepd 0x155551a0c700: error: *** JOB 31601200 ON batch-block7-00639 CANCELLED AT 2026-08-06T17:30:45 DUE TO TIME LIMIT ***
|
| 649 |
+
srun: Job step aborted: Waiting up to 122 seconds for job step to finish.
|
| 650 |
+
Aug 06 17:30:45.185763 1679643 slurmstepd 0x15555088f700: error: *** STEP 31601200.0 ON batch-block7-00639 CANCELLED AT 2026-08-06T17:30:45 DUE TO TIME LIMIT ***
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logs/scaleup_3_precompute_31601202.log
ADDED
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| 1 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/motioner.py:30: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 2 |
+
@amp.autocast(enabled=False)
|
| 3 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/motioner.py:30: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 4 |
+
@amp.autocast(enabled=False)
|
| 5 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/motioner.py:30: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 6 |
+
@amp.autocast(enabled=False)
|
| 7 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/motioner.py:41: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 8 |
+
@amp.autocast(enabled=False)
|
| 9 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/motioner.py:30: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 10 |
+
@amp.autocast(enabled=False)
|
| 11 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/motioner.py:41: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 12 |
+
@amp.autocast(enabled=False)
|
| 13 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/motioner.py:41: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 14 |
+
@amp.autocast(enabled=False)
|
| 15 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/motioner.py:30: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 16 |
+
@amp.autocast(enabled=False)
|
| 17 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/motioner.py:41: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 18 |
+
@amp.autocast(enabled=False)
|
| 19 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/motioner.py:30: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 20 |
+
@amp.autocast(enabled=False)
|
| 21 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/motioner.py:41: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 22 |
+
@amp.autocast(enabled=False)
|
| 23 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/motioner.py:41: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 24 |
+
@amp.autocast(enabled=False)
|
| 25 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/motioner.py:30: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 26 |
+
@amp.autocast(enabled=False)
|
| 27 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/motioner.py:41: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 28 |
+
@amp.autocast(enabled=False)
|
| 29 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/motioner.py:30: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 30 |
+
@amp.autocast(enabled=False)
|
| 31 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/motioner.py:41: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 32 |
+
@amp.autocast(enabled=False)
|
| 33 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/model_s2v.py:61: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 34 |
+
@amp.autocast(enabled=False)
|
| 35 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/model_s2v.py:61: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 36 |
+
@amp.autocast(enabled=False)
|
| 37 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/model_s2v.py:61: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 38 |
+
@amp.autocast(enabled=False)
|
| 39 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/model_s2v.py:61: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 40 |
+
@amp.autocast(enabled=False)
|
| 41 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/model_s2v.py:79: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 42 |
+
@amp.autocast(enabled=False)
|
| 43 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/model_s2v.py:61: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 44 |
+
@amp.autocast(enabled=False)
|
| 45 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/model_s2v.py:79: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 46 |
+
@amp.autocast(enabled=False)
|
| 47 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/model_s2v.py:79: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 48 |
+
@amp.autocast(enabled=False)
|
| 49 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/model_s2v.py:61: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 50 |
+
@amp.autocast(enabled=False)
|
| 51 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/model_s2v.py:79: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 52 |
+
@amp.autocast(enabled=False)
|
| 53 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/model_s2v.py:61: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
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| 54 |
+
@amp.autocast(enabled=False)
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| 55 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/model_s2v.py:79: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
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| 56 |
+
@amp.autocast(enabled=False)
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| 57 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/model_s2v.py:79: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
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| 58 |
+
@amp.autocast(enabled=False)
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| 59 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/model_s2v.py:79: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
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| 60 |
+
@amp.autocast(enabled=False)
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| 61 |
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/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/model_s2v.py:61: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
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| 62 |
+
@amp.autocast(enabled=False)
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| 63 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/s2v/model_s2v.py:79: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
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| 64 |
+
@amp.autocast(enabled=False)
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| 65 |
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/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/vae2_1.py:614: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
|
| 66 |
+
torch.load(pretrained_path, map_location=device), assign=True)
|
| 67 |
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/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/vae2_1.py:614: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
|
| 68 |
+
torch.load(pretrained_path, map_location=device), assign=True)
|
| 69 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/vae2_1.py:614: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
|
| 70 |
+
torch.load(pretrained_path, map_location=device), assign=True)
|
| 71 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/vae2_1.py:614: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
|
| 72 |
+
torch.load(pretrained_path, map_location=device), assign=True)
|
| 73 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/vae2_1.py:614: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
|
| 74 |
+
torch.load(pretrained_path, map_location=device), assign=True)
|
| 75 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/vae2_1.py:614: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
|
| 76 |
+
torch.load(pretrained_path, map_location=device), assign=True)
|
| 77 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/vae2_1.py:614: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
|
| 78 |
+
torch.load(pretrained_path, map_location=device), assign=True)
|
| 79 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/vae2_1.py:614: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
|
| 80 |
+
torch.load(pretrained_path, map_location=device), assign=True)
|
| 81 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/t5.py:496: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
|
| 82 |
+
model.load_state_dict(torch.load(checkpoint_path, map_location='cpu'))
|
| 83 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/t5.py:496: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
|
| 84 |
+
model.load_state_dict(torch.load(checkpoint_path, map_location='cpu'))
|
| 85 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/t5.py:496: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
|
| 86 |
+
model.load_state_dict(torch.load(checkpoint_path, map_location='cpu'))
|
| 87 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/t5.py:496: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
|
| 88 |
+
model.load_state_dict(torch.load(checkpoint_path, map_location='cpu'))
|
| 89 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/t5.py:496: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
|
| 90 |
+
model.load_state_dict(torch.load(checkpoint_path, map_location='cpu'))
|
| 91 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/t5.py:496: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
|
| 92 |
+
model.load_state_dict(torch.load(checkpoint_path, map_location='cpu'))
|
| 93 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/t5.py:496: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
|
| 94 |
+
model.load_state_dict(torch.load(checkpoint_path, map_location='cpu'))
|
| 95 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/t5.py:496: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
|
| 96 |
+
model.load_state_dict(torch.load(checkpoint_path, map_location='cpu'))
|
| 97 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/vae2_1.py:651: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 98 |
+
with amp.autocast(dtype=self.dtype):
|
| 99 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/vae2_1.py:651: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 100 |
+
with amp.autocast(dtype=self.dtype):
|
| 101 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/vae2_1.py:651: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 102 |
+
with amp.autocast(dtype=self.dtype):
|
| 103 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/vae2_1.py:651: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 104 |
+
with amp.autocast(dtype=self.dtype):
|
| 105 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/vae2_1.py:651: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 106 |
+
with amp.autocast(dtype=self.dtype):
|
| 107 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/vae2_1.py:651: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 108 |
+
with amp.autocast(dtype=self.dtype):
|
| 109 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/vae2_1.py:651: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 110 |
+
with amp.autocast(dtype=self.dtype):
|
| 111 |
+
/lustre/fs11/portfolios/healthcareeng/projects/healthcareeng_computervision/users/yuq/project/cheng/viper/third_party/Wan2.2/wan/modules/vae2_1.py:651: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
|
| 112 |
+
with amp.autocast(dtype=self.dtype):
|
| 113 |
+
[1] 20/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(26, 4096)
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[0] 20/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(40, 4096)
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[5] 20/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(26, 4096)
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[2] 20/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(36, 4096)
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[3] 20/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(40, 4096)
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[7] 20/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(24, 4096)
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[6] 20/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(32, 4096)
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[4] 20/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(36, 4096)
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[0] 40/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(30, 4096)
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[5] 40/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(37, 4096)
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[2] 40/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(36, 4096)
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[7] 40/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(28, 4096)
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[6] 40/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(27, 4096)
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[4] 40/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(34, 4096)
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[4] 60/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(28, 4096)
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| 137 |
+
[1] 80/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(40, 4096)
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| 138 |
+
[0] 80/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(39, 4096)
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| 139 |
+
[5] 80/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(34, 4096)
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| 140 |
+
[2] 80/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(32, 4096)
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| 141 |
+
[3] 80/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(27, 4096)
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| 142 |
+
[6] 80/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(28, 4096)
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| 143 |
+
[7] 80/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(30, 4096)
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| 144 |
+
[4] 80/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(35, 4096)
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| 145 |
+
[1] 100/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(45, 4096)
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| 146 |
+
[0] 100/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(29, 4096)
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| 147 |
+
[5] 100/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(27, 4096)
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| 148 |
+
[2] 100/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(34, 4096)
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| 149 |
+
[3] 100/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(35, 4096)
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| 150 |
+
[6] 100/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(34, 4096)
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| 151 |
+
[7] 100/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(28, 4096)
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| 152 |
+
[4] 100/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(39, 4096)
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| 153 |
+
[0] 120/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(32, 4096)
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| 154 |
+
[1] 140/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(46, 4096)
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| 155 |
+
[2] 140/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(33, 4096)
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| 156 |
+
[5] 140/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(31, 4096)
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| 157 |
+
[0] 140/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(35, 4096)
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| 158 |
+
[3] 140/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(34, 4096)
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| 159 |
+
[6] 140/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(32, 4096)
|
| 160 |
+
[7] 140/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(27, 4096)
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| 161 |
+
[4] 140/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(32, 4096)
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| 162 |
+
[1] 160/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(25, 4096)
|
| 163 |
+
[2] 160/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(29, 4096)
|
| 164 |
+
[5] 160/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(23, 4096)
|
| 165 |
+
[3] 160/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(25, 4096)
|
| 166 |
+
[0] 160/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(26, 4096)
|
| 167 |
+
[6] 160/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(31, 4096)
|
| 168 |
+
[7] 160/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(34, 4096)
|
| 169 |
+
[4] 160/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(31, 4096)
|
| 170 |
+
[1] 180/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(39, 4096)
|
| 171 |
+
[2] 180/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(24, 4096)
|
| 172 |
+
[5] 180/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(27, 4096)
|
| 173 |
+
[3] 180/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(20, 4096)
|
| 174 |
+
[0] 180/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(34, 4096)
|
| 175 |
+
[6] 180/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(32, 4096)
|
| 176 |
+
[7] 180/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(24, 4096)
|
| 177 |
+
[4] 180/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(32, 4096)
|
| 178 |
+
[1] 200/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(29, 4096)
|
| 179 |
+
[2] 200/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(36, 4096)
|
| 180 |
+
[5] 200/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(32, 4096)
|
| 181 |
+
[3] 200/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(27, 4096)
|
| 182 |
+
[6] 200/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(27, 4096)
|
| 183 |
+
[0] 200/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(35, 4096)
|
| 184 |
+
[7] 200/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(33, 4096)
|
| 185 |
+
[4] 200/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(20, 4096)
|
| 186 |
+
[1] 220/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(38, 4096)
|
| 187 |
+
[2] 220/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(29, 4096)
|
| 188 |
+
[5] 220/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(25, 4096)
|
| 189 |
+
[3] 220/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(32, 4096)
|
| 190 |
+
[6] 220/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(38, 4096)
|
| 191 |
+
[0] 220/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(27, 4096)
|
| 192 |
+
[7] 220/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(30, 4096)
|
| 193 |
+
[4] 220/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(34, 4096)
|
| 194 |
+
[1] 240/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(27, 4096)
|
| 195 |
+
[2] 240/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(26, 4096)
|
| 196 |
+
[0] 240/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(32, 4096)
|
| 197 |
+
[1] 260/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(36, 4096)
|
| 198 |
+
[2] 260/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(48, 4096)
|
| 199 |
+
[5] 260/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(29, 4096)
|
| 200 |
+
[3] 260/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(21, 4096)
|
| 201 |
+
[6] 260/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(33, 4096)
|
| 202 |
+
[0] 260/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(31, 4096)
|
| 203 |
+
[4] 260/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(35, 4096)
|
| 204 |
+
[7] 260/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(28, 4096)
|
| 205 |
+
[1] 280/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(39, 4096)
|
| 206 |
+
[2] 280/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(39, 4096)
|
| 207 |
+
[5] 280/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(39, 4096)
|
| 208 |
+
[3] 280/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(36, 4096)
|
| 209 |
+
[6] 280/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(30, 4096)
|
| 210 |
+
[0] 280/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(30, 4096)
|
| 211 |
+
[4] 280/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(35, 4096)
|
| 212 |
+
[7] 280/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(29, 4096)
|
| 213 |
+
[2] 300/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(23, 4096)
|
| 214 |
+
[1] 300/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(24, 4096)
|
| 215 |
+
[5] 300/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(28, 4096)
|
| 216 |
+
[3] 300/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(30, 4096)
|
| 217 |
+
[6] 300/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(34, 4096)
|
| 218 |
+
[0] 300/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(38, 4096)
|
| 219 |
+
[4] 300/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(30, 4096)
|
| 220 |
+
[7] 300/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(34, 4096)
|
| 221 |
+
[2] 320/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(37, 4096)
|
| 222 |
+
[1] 320/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(31, 4096)
|
| 223 |
+
[5] 320/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(28, 4096)
|
| 224 |
+
[3] 320/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(32, 4096)
|
| 225 |
+
[6] 320/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(25, 4096)
|
| 226 |
+
[0] 320/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(31, 4096)
|
| 227 |
+
[4] 320/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(37, 4096)
|
| 228 |
+
[7] 320/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(32, 4096)
|
| 229 |
+
[2] 340/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(35, 4096)
|
| 230 |
+
[1] 340/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(19, 4096)
|
| 231 |
+
[5] 340/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(34, 4096)
|
| 232 |
+
[3] 340/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(44, 4096)
|
| 233 |
+
[6] 340/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(52, 4096)
|
| 234 |
+
[0] 340/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(27, 4096)
|
| 235 |
+
[4] 340/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(34, 4096)
|
| 236 |
+
[7] 340/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(33, 4096)
|
| 237 |
+
[2] 360/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(27, 4096)
|
| 238 |
+
[1] 360/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(31, 4096)
|
| 239 |
+
[3] 360/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(31, 4096)
|
| 240 |
+
[0] 360/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(32, 4096)
|
| 241 |
+
[2] 380/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(27, 4096)
|
| 242 |
+
[5] 380/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(34, 4096)
|
| 243 |
+
[1] 380/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(31, 4096)
|
| 244 |
+
[3] 380/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(37, 4096)
|
| 245 |
+
[6] 380/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(34, 4096)
|
| 246 |
+
[4] 380/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(26, 4096)
|
| 247 |
+
[0] 380/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(43, 4096)
|
| 248 |
+
[7] 380/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(32, 4096)
|
| 249 |
+
[2] 400/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(39, 4096)
|
| 250 |
+
[5] 400/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(35, 4096)
|
| 251 |
+
[1] 400/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(29, 4096)
|
| 252 |
+
[3] 400/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(39, 4096)
|
| 253 |
+
[6] 400/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(32, 4096)
|
| 254 |
+
[4] 400/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(25, 4096)
|
| 255 |
+
[0] 400/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(39, 4096)
|
| 256 |
+
[7] 400/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(41, 4096)
|
| 257 |
+
[2] 420/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(27, 4096)
|
| 258 |
+
[5] 420/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(27, 4096)
|
| 259 |
+
[1] 420/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(25, 4096)
|
| 260 |
+
[3] 420/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(43, 4096)
|
| 261 |
+
[6] 420/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(38, 4096)
|
| 262 |
+
[0] 420/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(27, 4096)
|
| 263 |
+
[4] 420/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(33, 4096)
|
| 264 |
+
[7] 420/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(37, 4096)
|
| 265 |
+
[2] 440/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(31, 4096)
|
| 266 |
+
[5] 440/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(32, 4096)
|
| 267 |
+
[1] 440/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(33, 4096)
|
| 268 |
+
[3] 440/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(35, 4096)
|
| 269 |
+
[6] 440/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(35, 4096)
|
| 270 |
+
[0] 440/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(42, 4096)
|
| 271 |
+
[4] 440/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(33, 4096)
|
| 272 |
+
[7] 440/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(34, 4096)
|
| 273 |
+
[2] 460/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(27, 4096)
|
| 274 |
+
[5] 460/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(39, 4096)
|
| 275 |
+
[1] 460/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(31, 4096)
|
| 276 |
+
[3] 460/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(31, 4096)
|
| 277 |
+
[6] 460/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(24, 4096)
|
| 278 |
+
[0] 460/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(39, 4096)
|
| 279 |
+
[4] 460/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(36, 4096)
|
| 280 |
+
[7] 460/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(42, 4096)
|
| 281 |
+
[2] 480/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(23, 4096)
|
| 282 |
+
[1] 480/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(21, 4096)
|
| 283 |
+
[0] 480/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(33, 4096)
|
| 284 |
+
[2] 500/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(34, 4096)
|
| 285 |
+
[5] 500/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(42, 4096)
|
| 286 |
+
[3] 500/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(26, 4096)
|
| 287 |
+
[1] 500/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(36, 4096)
|
| 288 |
+
[6] 500/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(36, 4096)
|
| 289 |
+
[0] 500/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(38, 4096)
|
| 290 |
+
[4] 500/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(37, 4096)
|
| 291 |
+
[7] 500/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(32, 4096)
|
| 292 |
+
[2] 520/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(29, 4096)
|
| 293 |
+
[5] 520/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(26, 4096)
|
| 294 |
+
[3] 520/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(28, 4096)
|
| 295 |
+
[1] 520/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(38, 4096)
|
| 296 |
+
[6] 520/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(32, 4096)
|
| 297 |
+
[0] 520/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(32, 4096)
|
| 298 |
+
[4] 520/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(36, 4096)
|
| 299 |
+
[7] 520/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(58, 4096)
|
| 300 |
+
[2] 540/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(33, 4096)
|
| 301 |
+
[5] 540/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(39, 4096)
|
| 302 |
+
[3] 540/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(36, 4096)
|
| 303 |
+
[1] 540/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(50, 4096)
|
| 304 |
+
[6] 540/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(35, 4096)
|
| 305 |
+
[0] 540/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(31, 4096)
|
| 306 |
+
[4] 540/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(32, 4096)
|
| 307 |
+
[7] 540/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(30, 4096)
|
| 308 |
+
[2] 560/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(35, 4096)
|
| 309 |
+
[5] 560/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(33, 4096)
|
| 310 |
+
[3] 560/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(38, 4096)
|
| 311 |
+
[1] 560/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(27, 4096)
|
| 312 |
+
[6] 560/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(39, 4096)
|
| 313 |
+
[0] 560/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(40, 4096)
|
| 314 |
+
[4] 560/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(43, 4096)
|
| 315 |
+
[7] 560/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(31, 4096)
|
| 316 |
+
[2] 580/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(26, 4096)
|
| 317 |
+
[5] 580/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(34, 4096)
|
| 318 |
+
[3] 580/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(46, 4096)
|
| 319 |
+
[1] 580/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(51, 4096)
|
| 320 |
+
[6] 580/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(30, 4096)
|
| 321 |
+
[0] 580/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(25, 4096)
|
| 322 |
+
[4] 580/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(32, 4096)
|
| 323 |
+
[7] 580/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(33, 4096)
|
| 324 |
+
[2] 600/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(26, 4096)
|
| 325 |
+
[3] 600/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(34, 4096)
|
| 326 |
+
[1] 600/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(29, 4096)
|
| 327 |
+
[0] 600/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(31, 4096)
|
| 328 |
+
[2] 620/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(28, 4096)
|
| 329 |
+
[5] 620/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(34, 4096)
|
| 330 |
+
[3] 620/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(22, 4096)
|
| 331 |
+
[1] 620/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(34, 4096)
|
| 332 |
+
[6] 620/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(33, 4096)
|
| 333 |
+
[4] 620/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(31, 4096)
|
| 334 |
+
[0] 620/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(32, 4096)
|
| 335 |
+
[7] 620/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(37, 4096)
|
| 336 |
+
[2] 640/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(42, 4096)
|
| 337 |
+
[5] 640/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(24, 4096)
|
| 338 |
+
[3] 640/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(34, 4096)
|
| 339 |
+
[1] 640/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(24, 4096)
|
| 340 |
+
[6] 640/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(41, 4096)
|
| 341 |
+
[4] 640/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(31, 4096)
|
| 342 |
+
[0] 640/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(25, 4096)
|
| 343 |
+
[7] 640/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(23, 4096)
|
| 344 |
+
[2] 660/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(33, 4096)
|
| 345 |
+
[5] 660/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(44, 4096)
|
| 346 |
+
[3] 660/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(38, 4096)
|
| 347 |
+
[1] 660/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(32, 4096)
|
| 348 |
+
[6] 660/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(32, 4096)
|
| 349 |
+
[4] 660/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(29, 4096)
|
| 350 |
+
[0] 660/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(43, 4096)
|
| 351 |
+
[7] 660/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(34, 4096)
|
| 352 |
+
[2] 680/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(37, 4096)
|
| 353 |
+
[5] 680/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(37, 4096)
|
| 354 |
+
[3] 680/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(27, 4096)
|
| 355 |
+
[1] 680/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(27, 4096)
|
| 356 |
+
[6] 680/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(29, 4096)
|
| 357 |
+
[4] 680/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(34, 4096)
|
| 358 |
+
[0] 680/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(27, 4096)
|
| 359 |
+
[7] 680/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(33, 4096)
|
| 360 |
+
[2] 700/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(28, 4096)
|
| 361 |
+
[5] 700/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(29, 4096)
|
| 362 |
+
[3] 700/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(45, 4096)
|
| 363 |
+
[1] 700/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(28, 4096)
|
| 364 |
+
[6] 700/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(39, 4096)
|
| 365 |
+
[4] 700/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(30, 4096)
|
| 366 |
+
[0] 700/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(29, 4096)
|
| 367 |
+
[7] 700/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(37, 4096)
|
| 368 |
+
[2] 720/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(31, 4096)
|
| 369 |
+
[5] 720/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(26, 4096)
|
| 370 |
+
[3] 720/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(32, 4096)
|
| 371 |
+
[1] 720/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(33, 4096)
|
| 372 |
+
[6] 720/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(23, 4096)
|
| 373 |
+
[4] 720/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(28, 4096)
|
| 374 |
+
[0] 720/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(32, 4096)
|
| 375 |
+
[2] 740/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(28, 4096)
|
| 376 |
+
[5] 740/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(36, 4096)
|
| 377 |
+
[3] 740/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(30, 4096)
|
| 378 |
+
[1] 740/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(38, 4096)
|
| 379 |
+
[6] 740/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(25, 4096)
|
| 380 |
+
[4] 740/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(33, 4096)
|
| 381 |
+
[0] 740/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(40, 4096)
|
| 382 |
+
[7] 740/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(34, 4096)
|
| 383 |
+
[2] 760/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(42, 4096)
|
| 384 |
+
[5] 760/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(28, 4096)
|
| 385 |
+
[3] 760/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(33, 4096)
|
| 386 |
+
[1] 760/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(30, 4096)
|
| 387 |
+
[6] 760/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(30, 4096)
|
| 388 |
+
[4] 760/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(27, 4096)
|
| 389 |
+
[0] 760/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(30, 4096)
|
| 390 |
+
[7] 760/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(31, 4096)
|
| 391 |
+
[2] 780/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(36, 4096)
|
| 392 |
+
[5] 780/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(31, 4096)
|
| 393 |
+
[3] 780/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(38, 4096)
|
| 394 |
+
[1] 780/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(38, 4096)
|
| 395 |
+
[6] 780/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(36, 4096)
|
| 396 |
+
[4] 780/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(31, 4096)
|
| 397 |
+
[0] 780/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(25, 4096)
|
| 398 |
+
[7] 780/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(31, 4096)
|
| 399 |
+
[2] 800/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(29, 4096)
|
| 400 |
+
[5] 800/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(30, 4096)
|
| 401 |
+
[3] 800/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(32, 4096)
|
| 402 |
+
[1] 800/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(30, 4096)
|
| 403 |
+
[6] 800/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(29, 4096)
|
| 404 |
+
[4] 800/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(24, 4096)
|
| 405 |
+
[0] 800/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(31, 4096)
|
| 406 |
+
[7] 800/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(35, 4096)
|
| 407 |
+
[2] 820/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(34, 4096)
|
| 408 |
+
[5] 820/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(27, 4096)
|
| 409 |
+
[3] 820/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(34, 4096)
|
| 410 |
+
[1] 820/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(28, 4096)
|
| 411 |
+
[6] 820/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(33, 4096)
|
| 412 |
+
[4] 820/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(40, 4096)
|
| 413 |
+
[0] 820/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(36, 4096)
|
| 414 |
+
[7] 820/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(28, 4096)
|
| 415 |
+
[2] 840/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(34, 4096)
|
| 416 |
+
[5] 840/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(38, 4096)
|
| 417 |
+
[3] 840/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(23, 4096)
|
| 418 |
+
[1] 840/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(46, 4096)
|
| 419 |
+
[6] 840/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(30, 4096)
|
| 420 |
+
[4] 840/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(35, 4096)
|
| 421 |
+
[0] 840/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(40, 4096)
|
| 422 |
+
[7] 840/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(28, 4096)
|
| 423 |
+
[2] 860/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(24, 4096)
|
| 424 |
+
[5] 860/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(39, 4096)
|
| 425 |
+
[3] 860/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(47, 4096)
|
| 426 |
+
[1] 860/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(34, 4096)
|
| 427 |
+
[6] 860/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(34, 4096)
|
| 428 |
+
[4] 860/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(40, 4096)
|
| 429 |
+
[0] 860/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(34, 4096)
|
| 430 |
+
[7] 860/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(26, 4096)
|
| 431 |
+
[2] 880/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(26, 4096)
|
| 432 |
+
[5] 880/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(20, 4096)
|
| 433 |
+
[3] 880/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(32, 4096)
|
| 434 |
+
[1] 880/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(28, 4096)
|
| 435 |
+
[6] 880/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(38, 4096)
|
| 436 |
+
[0] 880/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(24, 4096)
|
| 437 |
+
[4] 880/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(32, 4096)
|
| 438 |
+
[7] 880/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(28, 4096)
|
| 439 |
+
[2] 900/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(29, 4096)
|
| 440 |
+
[5] 900/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(25, 4096)
|
| 441 |
+
[3] 900/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(39, 4096)
|
| 442 |
+
[1] 900/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(37, 4096)
|
| 443 |
+
[6] 900/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(36, 4096)
|
| 444 |
+
[0] 900/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(31, 4096)
|
| 445 |
+
[4] 900/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(35, 4096)
|
| 446 |
+
[7] 900/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(24, 4096)
|
| 447 |
+
[2] 920/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(32, 4096)
|
| 448 |
+
[5] 920/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(44, 4096)
|
| 449 |
+
[3] 920/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(23, 4096)
|
| 450 |
+
[1] 920/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(37, 4096)
|
| 451 |
+
[6] 920/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(25, 4096)
|
| 452 |
+
[0] 920/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(33, 4096)
|
| 453 |
+
[4] 920/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(29, 4096)
|
| 454 |
+
[7] 920/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(28, 4096)
|
| 455 |
+
[2] 940/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(31, 4096)
|
| 456 |
+
[5] 940/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(29, 4096)
|
| 457 |
+
[3] 940/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(33, 4096)
|
| 458 |
+
[1] 940/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(29, 4096)
|
| 459 |
+
[6] 940/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(37, 4096)
|
| 460 |
+
[0] 940/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(28, 4096)
|
| 461 |
+
[4] 940/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(27, 4096)
|
| 462 |
+
[7] 940/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(36, 4096)
|
| 463 |
+
[2] 960/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(29, 4096)
|
| 464 |
+
[5] 960/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(29, 4096)
|
| 465 |
+
[5] DONE 961/961 -> data/cache
|
| 466 |
+
[3] 960/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(39, 4096)
|
| 467 |
+
[2] DONE 962/962 -> data/cache
|
| 468 |
+
[3] DONE 962/962 -> data/cache
|
| 469 |
+
[1] 960/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(28, 4096)
|
| 470 |
+
[1] DONE 962/962 -> data/cache
|
| 471 |
+
[6] 960/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(27, 4096)
|
| 472 |
+
[6] DONE 961/961 -> data/cache
|
| 473 |
+
[0] 960/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(24, 4096)
|
| 474 |
+
[4] 960/962 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(33, 4096)
|
| 475 |
+
[7] 960/961 lat=(16, 21, 60, 104) y=(20, 21, 60, 104) ctx=(29, 4096)
|
| 476 |
+
[7] DONE 961/961 -> data/cache
|
| 477 |
+
[0] DONE 962/962 -> data/cache
|
| 478 |
+
[4] DONE 962/962 -> data/cache
|
| 479 |
+
cached clips: 7693 / 7693
|
scripts/eval_run.sbatch
ADDED
|
@@ -0,0 +1,37 @@
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|
|
|
|
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|
|
|
|
|
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|
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|
|
|
|
| 1 |
+
#!/bin/bash
|
| 2 |
+
#SBATCH --account=healthcareeng_computervision
|
| 3 |
+
#SBATCH --partition=backfill_singlenode
|
| 4 |
+
#SBATCH --nodes=1
|
| 5 |
+
#SBATCH --gpus-per-node=8
|
| 6 |
+
#SBATCH --exclusive
|
| 7 |
+
#SBATCH --time=24:00:00
|
| 8 |
+
#SBATCH --job-name=viper_eval
|
| 9 |
+
#SBATCH --output=logs/eval_run_%j.log
|
| 10 |
+
#
|
| 11 |
+
# Validation inference + evaluation, split out of full_run.sbatch.
|
| 12 |
+
#
|
| 13 |
+
# 74 held-out pairs x 2 variants (VIPER vs zeroed physics tokens) = 148 videos
|
| 14 |
+
# at 50 sampling steps / 81 frames / 480p, over 4 ranks of 2 GPUs. Set
|
| 15 |
+
# INFER_LIMIT to generate only the first N pairs if the queue is tight.
|
| 16 |
+
set -e
|
| 17 |
+
cd "$SLURM_SUBMIT_DIR"
|
| 18 |
+
source env.sh
|
| 19 |
+
|
| 20 |
+
export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
|
| 21 |
+
export PYTHONUNBUFFERED=1
|
| 22 |
+
|
| 23 |
+
CKPT=${CKPT:-checkpoints/stage3/final.pt}
|
| 24 |
+
|
| 25 |
+
echo "=== validation inference (VIPER vs no-physics) from $CKPT ==="
|
| 26 |
+
srun --ntasks=4 scripts/run_infer.sh "$CKPT"
|
| 27 |
+
cat results/manifest.jsonl.* > results/manifest.jsonl
|
| 28 |
+
|
| 29 |
+
echo "=== evaluating generated videos ==="
|
| 30 |
+
.venv/bin/python -m viper.eval --manifest results/manifest.jsonl \
|
| 31 |
+
--out results/eval_viper.json
|
| 32 |
+
|
| 33 |
+
echo "=== paired conditioning ablation (reference vs zeroed physics tokens) ==="
|
| 34 |
+
for STAGE in 1 2 3; do
|
| 35 |
+
.venv/bin/python -m viper.eval_loss --ckpt checkpoints/stage$STAGE/final.pt \
|
| 36 |
+
--out results/eval_loss_stage$STAGE.json
|
| 37 |
+
done
|
scripts/fetch_wisa.py
ADDED
|
@@ -0,0 +1,74 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
"""Fetch + extract the WISA-80K wan480p shards.
|
| 2 |
+
|
| 3 |
+
The clips live in ``csusupergear/WISA-80K-wan480p-16fps-81f`` as 276 webdataset
|
| 4 |
+
tars; shard ``N`` holds ids ``N*100 .. N*100+99`` as ``<id>.{mp4,txt,json}``.
|
| 5 |
+
Download and extraction are interleaved over a thread pool so a shard is unpacked
|
| 6 |
+
as soon as it lands. Both steps are idempotent: an already-downloaded shard is
|
| 7 |
+
served from the HF cache and an already-extracted shard is skipped.
|
| 8 |
+
"""
|
| 9 |
+
|
| 10 |
+
from __future__ import annotations
|
| 11 |
+
|
| 12 |
+
import argparse
|
| 13 |
+
import os
|
| 14 |
+
import tarfile
|
| 15 |
+
from concurrent.futures import ThreadPoolExecutor, as_completed
|
| 16 |
+
from pathlib import Path
|
| 17 |
+
|
| 18 |
+
REPO = "csusupergear/WISA-80K-wan480p-16fps-81f"
|
| 19 |
+
|
| 20 |
+
|
| 21 |
+
def shard_done(vdir: Path, shard: int) -> bool:
|
| 22 |
+
"""A shard counts as extracted when its last clip's three files exist."""
|
| 23 |
+
last = f"{shard * 100 + 99:08d}"
|
| 24 |
+
return all((vdir / f"{last}.{ext}").exists() for ext in ("mp4", "txt", "json"))
|
| 25 |
+
|
| 26 |
+
|
| 27 |
+
def fetch_one(shard: int, raw_dir: Path, vdir: Path) -> tuple[int, str]:
|
| 28 |
+
from huggingface_hub import hf_hub_download
|
| 29 |
+
|
| 30 |
+
if shard_done(vdir, shard):
|
| 31 |
+
return shard, "skip"
|
| 32 |
+
path = hf_hub_download(
|
| 33 |
+
REPO, f"{shard:06d}.tar", repo_type="dataset",
|
| 34 |
+
local_dir=str(raw_dir),
|
| 35 |
+
)
|
| 36 |
+
with tarfile.open(path) as tf:
|
| 37 |
+
tf.extractall(vdir)
|
| 38 |
+
return shard, "ok"
|
| 39 |
+
|
| 40 |
+
|
| 41 |
+
def main():
|
| 42 |
+
ap = argparse.ArgumentParser()
|
| 43 |
+
ap.add_argument("--num_shards", type=int, default=276)
|
| 44 |
+
ap.add_argument("--raw_dir", default="data/raw/wisa_wan480p")
|
| 45 |
+
ap.add_argument("--video_dir", default="data/videos")
|
| 46 |
+
ap.add_argument("--workers", type=int, default=8)
|
| 47 |
+
args = ap.parse_args()
|
| 48 |
+
|
| 49 |
+
raw_dir, vdir = Path(args.raw_dir), Path(args.video_dir)
|
| 50 |
+
vdir.mkdir(parents=True, exist_ok=True)
|
| 51 |
+
|
| 52 |
+
done = n_err = 0
|
| 53 |
+
with ThreadPoolExecutor(args.workers) as ex:
|
| 54 |
+
futs = {ex.submit(fetch_one, s, raw_dir, vdir): s
|
| 55 |
+
for s in range(args.num_shards)}
|
| 56 |
+
for f in as_completed(futs):
|
| 57 |
+
s = futs[f]
|
| 58 |
+
try:
|
| 59 |
+
_, status = f.result()
|
| 60 |
+
except Exception as e: # network / corrupt tar
|
| 61 |
+
n_err += 1
|
| 62 |
+
print(f"[err] shard {s}: {e}", flush=True)
|
| 63 |
+
continue
|
| 64 |
+
done += 1
|
| 65 |
+
if done % 10 == 0:
|
| 66 |
+
print(f"{done}/{args.num_shards} shards ({status} {s:06d})",
|
| 67 |
+
flush=True)
|
| 68 |
+
|
| 69 |
+
n = len(list(vdir.glob("*.mp4")))
|
| 70 |
+
print(f"DONE shards={done} errors={n_err} clips={n}", flush=True)
|
| 71 |
+
|
| 72 |
+
|
| 73 |
+
if __name__ == "__main__":
|
| 74 |
+
main()
|
scripts/full_run.sbatch
CHANGED
|
@@ -8,16 +8,20 @@
|
|
| 8 |
#SBATCH --job-name=viper_train
|
| 9 |
#SBATCH --output=logs/full_run_%j.log
|
| 10 |
#
|
| 11 |
-
# Three-stage VIPER training
|
|
|
|
|
|
|
|
|
|
| 12 |
#
|
| 13 |
-
# Step counts are scaled
|
| 14 |
-
#
|
| 15 |
-
#
|
| 16 |
-
# We keep the
|
| 17 |
-
#
|
| 18 |
#
|
| 19 |
# Throughput reference: 35.8 s/optimizer-step measured on one A100-80GB at
|
| 20 |
-
# 81x480x832 (seq_len 32760). With 8 ranks that is 8 samples per step
|
|
|
|
| 21 |
set -e
|
| 22 |
cd "$SLURM_SUBMIT_DIR"
|
| 23 |
source env.sh
|
|
@@ -25,20 +29,11 @@ source env.sh
|
|
| 25 |
export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
|
| 26 |
export PYTHONUNBUFFERED=1
|
| 27 |
|
| 28 |
-
|
| 29 |
-
|
| 30 |
-
|
|
|
|
|
|
|
|
|
|
| 31 |
|
| 32 |
-
echo "=== training complete:
|
| 33 |
-
srun --ntasks=4 scripts/run_infer.sh checkpoints/stage3/final.pt
|
| 34 |
-
cat results/manifest.jsonl.* > results/manifest.jsonl
|
| 35 |
-
|
| 36 |
-
echo "=== evaluating generated videos ==="
|
| 37 |
-
.venv/bin/python -m viper.eval --manifest results/manifest.jsonl \
|
| 38 |
-
--out results/eval_viper.json
|
| 39 |
-
|
| 40 |
-
echo "=== paired conditioning ablation (reference vs zeroed physics tokens) ==="
|
| 41 |
-
for STAGE in 1 2 3; do
|
| 42 |
-
.venv/bin/python -m viper.eval_loss --ckpt checkpoints/stage$STAGE/final.pt \
|
| 43 |
-
--out results/eval_loss_stage$STAGE.json
|
| 44 |
-
done
|
|
|
|
| 8 |
#SBATCH --job-name=viper_train
|
| 9 |
#SBATCH --output=logs/full_run_%j.log
|
| 10 |
#
|
| 11 |
+
# Three-stage VIPER training. Inference + eval now live in eval_run.sbatch:
|
| 12 |
+
# after the scale-up the validation set is 74 pairs x 2 variants = 148 videos,
|
| 13 |
+
# which no longer fits the same 24h window as 17.4h of training, and a timeout
|
| 14 |
+
# would take the evaluation down with it.
|
| 15 |
#
|
| 16 |
+
# Step counts are still scaled down from the paper's 3K/6K/6K, but the binding
|
| 17 |
+
# constraint is now wall clock, not overfitting: after the full-WISA scale-up the
|
| 18 |
+
# dataset is ~7.7K clips / ~hundreds of pairs, so the old 400/200/200 schedule no
|
| 19 |
+
# longer even covers one epoch of stage 1. We keep the paper's 1 : 2 : 2 stage
|
| 20 |
+
# ratio and size the total to the 24h limit.
|
| 21 |
#
|
| 22 |
# Throughput reference: 35.8 s/optimizer-step measured on one A100-80GB at
|
| 23 |
+
# 81x480x832 (seq_len 32760). With 8 ranks that is 8 samples per step, so
|
| 24 |
+
# 350+700+700 = 1750 steps ~= 17.4h, leaving room for inference + eval.
|
| 25 |
set -e
|
| 26 |
cd "$SLURM_SUBMIT_DIR"
|
| 27 |
source env.sh
|
|
|
|
| 29 |
export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
|
| 30 |
export PYTHONUNBUFFERED=1
|
| 31 |
|
| 32 |
+
# Train on the split-restricted sets, never on data/annotated.jsonl or
|
| 33 |
+
# data/pairs.jsonl: those still contain the held-out validation videos, and
|
| 34 |
+
# viper.eval reports on exactly those.
|
| 35 |
+
STEPS=${STEPS1:-350} scripts/train.sh 1 --clips data/clips_train.jsonl
|
| 36 |
+
STEPS=${STEPS2:-700} scripts/train.sh 2 --pairs data/pairs_train.jsonl
|
| 37 |
+
STEPS=${STEPS3:-700} scripts/train.sh 3 --pairs data/pairs_train.jsonl
|
| 38 |
|
| 39 |
+
echo "=== training complete: checkpoints/stage{1,2,3}/final.pt ==="
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
scripts/make_splits.py
CHANGED
|
@@ -20,7 +20,14 @@ def main():
|
|
| 20 |
ap.add_argument("--pairs", default="data/pairs.jsonl")
|
| 21 |
ap.add_argument("--train_out", default="data/pairs_train.jsonl")
|
| 22 |
ap.add_argument("--val_out", default="data/pairs_val.jsonl")
|
| 23 |
-
ap.add_argument("--
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 24 |
ap.add_argument("--seed", type=int, default=0)
|
| 25 |
args = ap.parse_args()
|
| 26 |
|
|
@@ -50,11 +57,21 @@ def main():
|
|
| 50 |
for r in data:
|
| 51 |
f.write(json.dumps(r, ensure_ascii=False) + "\n")
|
| 52 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 53 |
dropped = len(rows) - len(train) - len(val)
|
| 54 |
print(f"videos: {len(vids)} ({len(val_vids)} held out)")
|
| 55 |
print(f"train pairs: {len(train)}")
|
| 56 |
print(f"val pairs: {len(val)}")
|
| 57 |
print(f"dropped (cross-split, would leak): {dropped}")
|
|
|
|
| 58 |
|
| 59 |
|
| 60 |
if __name__ == "__main__":
|
|
|
|
| 20 |
ap.add_argument("--pairs", default="data/pairs.jsonl")
|
| 21 |
ap.add_argument("--train_out", default="data/pairs_train.jsonl")
|
| 22 |
ap.add_argument("--val_out", default="data/pairs_val.jsonl")
|
| 23 |
+
ap.add_argument("--clips", default="data/annotated.jsonl",
|
| 24 |
+
help="stage-1 clip set to filter against the held-out videos")
|
| 25 |
+
ap.add_argument("--clips_train_out", default="data/clips_train.jsonl")
|
| 26 |
+
# 0.08 of ~930 judged pairs is 74 validation pairs -- the size the paper
|
| 27 |
+
# reports on. A larger fraction is actively harmful here: every held-out
|
| 28 |
+
# clip quarantines all the *training* pairs it appears in, so val_frac 0.15
|
| 29 |
+
# costs ~145 train pairs relative to 0.08 for validation we do not need.
|
| 30 |
+
ap.add_argument("--val_frac", type=float, default=0.08)
|
| 31 |
ap.add_argument("--seed", type=int, default=0)
|
| 32 |
args = ap.parse_args()
|
| 33 |
|
|
|
|
| 57 |
for r in data:
|
| 58 |
f.write(json.dumps(r, ensure_ascii=False) + "\n")
|
| 59 |
|
| 60 |
+
# Stage 1 trains on single clips with self-reference augmentation. It must
|
| 61 |
+
# not see the held-out videos either, or a validation pair's target has
|
| 62 |
+
# already been reconstructed during training.
|
| 63 |
+
clips = [json.loads(l) for l in open(args.clips)]
|
| 64 |
+
clips_train = [c for c in clips if c["id"] not in val_vids]
|
| 65 |
+
with open(args.clips_train_out, "w") as f:
|
| 66 |
+
for c in clips_train:
|
| 67 |
+
f.write(json.dumps(c, ensure_ascii=False) + "\n")
|
| 68 |
+
|
| 69 |
dropped = len(rows) - len(train) - len(val)
|
| 70 |
print(f"videos: {len(vids)} ({len(val_vids)} held out)")
|
| 71 |
print(f"train pairs: {len(train)}")
|
| 72 |
print(f"val pairs: {len(val)}")
|
| 73 |
print(f"dropped (cross-split, would leak): {dropped}")
|
| 74 |
+
print(f"stage-1 clips: {len(clips_train)} / {len(clips)}")
|
| 75 |
|
| 76 |
|
| 77 |
if __name__ == "__main__":
|
scripts/push_to_hf.py
CHANGED
|
@@ -93,6 +93,12 @@ def main():
|
|
| 93 |
ap.add_argument("--repo_type", default="model")
|
| 94 |
ap.add_argument("--token", default=os.environ.get("HF_PUSH_TOKEN"))
|
| 95 |
ap.add_argument("--dry_run", action="store_true")
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 96 |
args = ap.parse_args()
|
| 97 |
|
| 98 |
if not args.token:
|
|
@@ -154,6 +160,30 @@ def main():
|
|
| 154 |
commit_message="qualitative comparison videos "
|
| 155 |
"(reference / target GT / Wan2.2 baseline / VIPER)",
|
| 156 |
)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 157 |
print(f"done: https://huggingface.co/{args.repo}")
|
| 158 |
|
| 159 |
|
|
|
|
| 93 |
ap.add_argument("--repo_type", default="model")
|
| 94 |
ap.add_argument("--token", default=os.environ.get("HF_PUSH_TOKEN"))
|
| 95 |
ap.add_argument("--dry_run", action="store_true")
|
| 96 |
+
ap.add_argument(
|
| 97 |
+
"--with_videos", action="store_true",
|
| 98 |
+
help="also upload the clips actually referenced by data/pairs_train.jsonl "
|
| 99 |
+
"and data/pairs_val.jsonl (not all of data/videos, which is the full "
|
| 100 |
+
"raw WISA-80K download cache)",
|
| 101 |
+
)
|
| 102 |
args = ap.parse_args()
|
| 103 |
|
| 104 |
if not args.token:
|
|
|
|
| 160 |
commit_message="qualitative comparison videos "
|
| 161 |
"(reference / target GT / Wan2.2 baseline / VIPER)",
|
| 162 |
)
|
| 163 |
+
if args.with_videos:
|
| 164 |
+
import json as _json
|
| 165 |
+
ids = set()
|
| 166 |
+
for fn in ("data/pairs_train.jsonl", "data/pairs_val.jsonl"):
|
| 167 |
+
for line in (ROOT / fn).read_text().splitlines():
|
| 168 |
+
d = _json.loads(line)
|
| 169 |
+
ids.add(d["ref_id"])
|
| 170 |
+
ids.add(d["tgt_id"])
|
| 171 |
+
patterns = [f"{i}{ext}" for i in ids for ext in (".mp4", ".txt", ".json")]
|
| 172 |
+
vids_dir = ROOT / "data" / "videos"
|
| 173 |
+
size = sum((vids_dir / p).stat().st_size for p in patterns
|
| 174 |
+
if (vids_dir / p).exists())
|
| 175 |
+
print(f"\nuploading {len(ids)} clips referenced by pairs_train/pairs_val "
|
| 176 |
+
f"({size/1e9:.2f} GB) from data/videos ...")
|
| 177 |
+
api.upload_folder(
|
| 178 |
+
folder_path=str(vids_dir),
|
| 179 |
+
path_in_repo="data/videos",
|
| 180 |
+
repo_id=args.repo,
|
| 181 |
+
repo_type=args.repo_type,
|
| 182 |
+
allow_patterns=patterns,
|
| 183 |
+
commit_message="training clips referenced by pairs_train.jsonl / "
|
| 184 |
+
"pairs_val.jsonl (WISA-80K subset, apache-2.0)",
|
| 185 |
+
)
|
| 186 |
+
|
| 187 |
print(f"done: https://huggingface.co/{args.repo}")
|
| 188 |
|
| 189 |
|
scripts/run_infer.sh
CHANGED
|
@@ -8,5 +8,5 @@ export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
|
|
| 8 |
exec .venv/bin/python -m viper.infer_batch \
|
| 9 |
--ckpt "$CKPT" --pairs data/pairs_val.jsonl \
|
| 10 |
--out_dir results/videos --manifest results/manifest.jsonl \
|
| 11 |
-
--ablate_no_physics \
|
| 12 |
--shard $SLURM_LOCALID --num_shards $SLURM_NTASKS
|
|
|
|
| 8 |
exec .venv/bin/python -m viper.infer_batch \
|
| 9 |
--ckpt "$CKPT" --pairs data/pairs_val.jsonl \
|
| 10 |
--out_dir results/videos --manifest results/manifest.jsonl \
|
| 11 |
+
--ablate_no_physics --limit ${INFER_LIMIT:-0} \
|
| 12 |
--shard $SLURM_LOCALID --num_shards $SLURM_NTASKS
|
scripts/run_judge.sh
CHANGED
|
@@ -6,4 +6,6 @@ export CUDA_VISIBLE_DEVICES=$((SLURM_LOCALID*2)),$((SLURM_LOCALID*2+1))
|
|
| 6 |
exec .venv/bin/python -m viper.data.build_pairs \
|
| 7 |
--annotated data/annotated.jsonl --out data/pairs.jsonl \
|
| 8 |
--judge models/Qwen3-VL-32B-Instruct \
|
| 9 |
-
--
|
|
|
|
|
|
|
|
|
| 6 |
exec .venv/bin/python -m viper.data.build_pairs \
|
| 7 |
--annotated data/annotated.jsonl --out data/pairs.jsonl \
|
| 8 |
--judge models/Qwen3-VL-32B-Instruct \
|
| 9 |
+
--max_per_bucket ${MAX_PER_BUCKET:-60} --limit ${JUDGE_LIMIT:-0} \
|
| 10 |
+
--deadline_min ${JUDGE_DEADLINE_MIN:-0} \
|
| 11 |
+
--shard $SLURM_LOCALID --num_shards $SLURM_NTASKS "$@"
|
scripts/run_precompute.sh
CHANGED
|
@@ -1,7 +1,9 @@
|
|
| 1 |
#!/bin/bash
|
| 2 |
cd "$(dirname "$0")/.."
|
| 3 |
source env.sh
|
| 4 |
-
|
|
|
|
|
|
|
| 5 |
exec .venv/bin/python -m viper.precompute \
|
| 6 |
-
--clips data/annotated.jsonl --out_dir data/cache \
|
| 7 |
--shard $SLURM_LOCALID --num_shards $SLURM_NTASKS
|
|
|
|
| 1 |
#!/bin/bash
|
| 2 |
cd "$(dirname "$0")/.."
|
| 3 |
source env.sh
|
| 4 |
+
# GPU_STRIDE=2 leaves a spare card per rank (the 4-rank pilot ran that way);
|
| 5 |
+
# with 8 ranks the VAE + umT5 pair fits comfortably on one 80GB card each.
|
| 6 |
+
export CUDA_VISIBLE_DEVICES=$((SLURM_LOCALID*${GPU_STRIDE:-1}))
|
| 7 |
exec .venv/bin/python -m viper.precompute \
|
| 8 |
+
--clips ${CLIPS:-data/annotated.jsonl} --out_dir data/cache \
|
| 9 |
--shard $SLURM_LOCALID --num_shards $SLURM_NTASKS
|
scripts/scaleup_1_annotate.sbatch
ADDED
|
@@ -0,0 +1,40 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
#!/bin/bash
|
| 2 |
+
#SBATCH --account=healthcareeng_computervision
|
| 3 |
+
#SBATCH --partition=interactive_singlenode
|
| 4 |
+
#SBATCH --nodes=1
|
| 5 |
+
#SBATCH --gpus-per-node=8
|
| 6 |
+
#SBATCH --exclusive
|
| 7 |
+
#SBATCH --time=04:00:00
|
| 8 |
+
#SBATCH --job-name=viper_su1_annotate
|
| 9 |
+
#SBATCH --output=logs/scaleup_1_annotate_%j.log
|
| 10 |
+
#
|
| 11 |
+
# Scale-up step 1: clip filtering + 3-axis physics annotation over the FULL
|
| 12 |
+
# WISA-80K wan480p subset (276 shards / 27.6K clips) instead of the 20-shard
|
| 13 |
+
# / 2K-clip pilot.
|
| 14 |
+
#
|
| 15 |
+
# Filtering is CPU/decode-bound (the cut detector decodes every clip that passed
|
| 16 |
+
# the metadata checks) -> multiprocess over the node's cores.
|
| 17 |
+
# Annotation is Qwen3-VL-4B, one rank per GPU.
|
| 18 |
+
set -e
|
| 19 |
+
cd "$SLURM_SUBMIT_DIR"
|
| 20 |
+
source env.sh
|
| 21 |
+
export PYTHONUNBUFFERED=1
|
| 22 |
+
|
| 23 |
+
if [ -s data/filtered_clips.jsonl ] && [ -z "$FORCE_FILTER" ]; then
|
| 24 |
+
echo "=== clip filtering: reusing $(wc -l < data/filtered_clips.jsonl) rows ==="
|
| 25 |
+
else
|
| 26 |
+
echo "=== clip filtering ($(ls data/videos/*.mp4 | wc -l) clips on disk) ==="
|
| 27 |
+
.venv/bin/python -m viper.data.filter_clips \
|
| 28 |
+
--video_dir data/videos --out data/filtered_clips.jsonl --workers 64
|
| 29 |
+
fi
|
| 30 |
+
|
| 31 |
+
# NOTE: no --gpus-per-task here. run_annotate.sh picks its card with
|
| 32 |
+
# CUDA_VISIBLE_DEVICES=$SLURM_LOCALID, which needs all 8 GPUs visible to every
|
| 33 |
+
# task; --gpus-per-task=1 renumbers them to 0 and ranks 1-7 die with
|
| 34 |
+
# "No CUDA GPUs are available".
|
| 35 |
+
echo "=== 3-axis annotation (Qwen3-VL-4B, 8 ranks) ==="
|
| 36 |
+
srun --ntasks=8 scripts/run_annotate.sh
|
| 37 |
+
|
| 38 |
+
cat data/annotated.jsonl.[0-7] > data/annotated.jsonl
|
| 39 |
+
echo "filtered_clips: $(wc -l < data/filtered_clips.jsonl)"
|
| 40 |
+
echo "annotated: $(wc -l < data/annotated.jsonl)"
|
scripts/scaleup_2_pairs.sbatch
ADDED
|
@@ -0,0 +1,42 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
#!/bin/bash
|
| 2 |
+
#SBATCH --account=healthcareeng_computervision
|
| 3 |
+
#SBATCH --partition=interactive_singlenode
|
| 4 |
+
#SBATCH --nodes=1
|
| 5 |
+
#SBATCH --gpus-per-node=8
|
| 6 |
+
#SBATCH --exclusive
|
| 7 |
+
#SBATCH --time=04:00:00
|
| 8 |
+
#SBATCH --job-name=viper_su2_pairs
|
| 9 |
+
#SBATCH --output=logs/scaleup_2_pairs_%j.log
|
| 10 |
+
#
|
| 11 |
+
# Scale-up step 2: bucket the annotated clips into candidate pairs and run the
|
| 12 |
+
# Qwen3-VL-32B transferability judge over them (4 ranks x 2 GPUs).
|
| 13 |
+
#
|
| 14 |
+
# MAX_PER_BUCKET is raised from the pilot's 60 because the clip set is ~13x
|
| 15 |
+
# larger. The judge measures ~6 s/pair/rank (two 6-frame videos per call), so
|
| 16 |
+
# 4 ranks get through ~2.4K pairs/hour in total -- a full 15.6K candidate set
|
| 17 |
+
# does NOT fit the 4h interactive limit. JUDGE_DEADLINE_MIN makes each rank stop
|
| 18 |
+
# early and exit 0, so the concatenation + split below still run and the afterok
|
| 19 |
+
# dependents survive; whatever was judged by then is kept.
|
| 20 |
+
set -e
|
| 21 |
+
cd "$SLURM_SUBMIT_DIR"
|
| 22 |
+
source env.sh
|
| 23 |
+
export PYTHONUNBUFFERED=1
|
| 24 |
+
|
| 25 |
+
export MAX_PER_BUCKET=${MAX_PER_BUCKET:-400}
|
| 26 |
+
export JUDGE_LIMIT=${JUDGE_LIMIT:-16000}
|
| 27 |
+
export JUDGE_DEADLINE_MIN=${JUDGE_DEADLINE_MIN:-195}
|
| 28 |
+
|
| 29 |
+
echo "=== candidate pairs (bucketing only, for the record) ==="
|
| 30 |
+
.venv/bin/python -m viper.data.build_pairs \
|
| 31 |
+
--annotated data/annotated.jsonl --out data/pairs_candidates.jsonl \
|
| 32 |
+
--max_per_bucket $MAX_PER_BUCKET --skip_judge
|
| 33 |
+
|
| 34 |
+
echo "=== transferability judging (Qwen3-VL-32B, 4 ranks x 2 GPUs) ==="
|
| 35 |
+
srun --ntasks=4 scripts/run_judge.sh # no --gpus-per-task: run_judge.sh maps cards itself
|
| 36 |
+
|
| 37 |
+
cat data/pairs.jsonl.[0-3] > data/pairs.jsonl
|
| 38 |
+
echo "candidates: $(wc -l < data/pairs_candidates.jsonl)"
|
| 39 |
+
echo "judged-kept pairs: $(wc -l < data/pairs.jsonl)"
|
| 40 |
+
|
| 41 |
+
echo "=== video-disjoint split ==="
|
| 42 |
+
.venv/bin/python scripts/make_splits.py
|
scripts/scaleup_3_precompute.sbatch
ADDED
|
@@ -0,0 +1,24 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
#!/bin/bash
|
| 2 |
+
#SBATCH --account=healthcareeng_computervision
|
| 3 |
+
#SBATCH --partition=interactive_singlenode
|
| 4 |
+
#SBATCH --nodes=1
|
| 5 |
+
#SBATCH --gpus-per-node=8
|
| 6 |
+
#SBATCH --exclusive
|
| 7 |
+
#SBATCH --time=04:00:00
|
| 8 |
+
#SBATCH --job-name=viper_su3_precompute
|
| 9 |
+
#SBATCH --output=logs/scaleup_3_precompute_%j.log
|
| 10 |
+
#
|
| 11 |
+
# Scale-up step 3: cache the frozen VAE latents / I2V conditioning / umT5
|
| 12 |
+
# embeddings for every annotated clip (~9.2MB per clip). Independent of the
|
| 13 |
+
# pair judging, so this runs on its own node in parallel with step 2.
|
| 14 |
+
#
|
| 15 |
+
# Resumable: viper.precompute skips ids already present in data/cache, so a
|
| 16 |
+
# requeue after the 4h interactive limit just continues.
|
| 17 |
+
set -e
|
| 18 |
+
cd "$SLURM_SUBMIT_DIR"
|
| 19 |
+
source env.sh
|
| 20 |
+
export PYTHONUNBUFFERED=1
|
| 21 |
+
|
| 22 |
+
srun --ntasks=8 scripts/run_precompute.sh # no --gpus-per-task: the rank maps its own card
|
| 23 |
+
|
| 24 |
+
echo "cached clips: $(ls data/cache/*.pt | wc -l) / $(wc -l < data/annotated.jsonl)"
|
viper/data/build_pairs.py
CHANGED
|
@@ -23,6 +23,7 @@ import argparse
|
|
| 23 |
import json
|
| 24 |
import random
|
| 25 |
import re
|
|
|
|
| 26 |
from collections import defaultdict
|
| 27 |
from pathlib import Path
|
| 28 |
|
|
@@ -123,6 +124,9 @@ def build_candidates(rows, max_per_bucket, max_caption_sim, seed=0):
|
|
| 123 |
})
|
| 124 |
bucket_stats[str(key)] = len(pairs)
|
| 125 |
cands.extend(pairs)
|
|
|
|
|
|
|
|
|
|
| 126 |
return cands, bucket_stats
|
| 127 |
|
| 128 |
|
|
@@ -137,6 +141,12 @@ def main():
|
|
| 137 |
ap.add_argument("--num_frames", type=int, default=6)
|
| 138 |
ap.add_argument("--skip_judge", action="store_true",
|
| 139 |
help="write candidates without MLLM filtering (debug)")
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 140 |
ap.add_argument("--limit", type=int, default=0)
|
| 141 |
ap.add_argument("--shard", type=int, default=0)
|
| 142 |
ap.add_argument("--num_shards", type=int, default=1)
|
|
@@ -173,8 +183,12 @@ def main():
|
|
| 173 |
).eval()
|
| 174 |
|
| 175 |
kept = 0
|
|
|
|
| 176 |
with open(args.out, "w") as f:
|
| 177 |
for i, c in enumerate(cands):
|
|
|
|
|
|
|
|
|
|
| 178 |
try:
|
| 179 |
rf = sample_frames(c["ref_video"], args.num_frames)
|
| 180 |
tf = sample_frames(c["tgt_video"], args.num_frames)
|
|
|
|
| 23 |
import json
|
| 24 |
import random
|
| 25 |
import re
|
| 26 |
+
import time
|
| 27 |
from collections import defaultdict
|
| 28 |
from pathlib import Path
|
| 29 |
|
|
|
|
| 124 |
})
|
| 125 |
bucket_stats[str(key)] = len(pairs)
|
| 126 |
cands.extend(pairs)
|
| 127 |
+
# Candidates come out grouped by bucket; shuffle so that a later --limit
|
| 128 |
+
# keeps a bucket-balanced sample instead of only the first few buckets.
|
| 129 |
+
rng.shuffle(cands)
|
| 130 |
return cands, bucket_stats
|
| 131 |
|
| 132 |
|
|
|
|
| 141 |
ap.add_argument("--num_frames", type=int, default=6)
|
| 142 |
ap.add_argument("--skip_judge", action="store_true",
|
| 143 |
help="write candidates without MLLM filtering (debug)")
|
| 144 |
+
ap.add_argument("--deadline_min", type=float, default=0,
|
| 145 |
+
help="stop judging after this many minutes and exit cleanly; "
|
| 146 |
+
"0 disables. The judge runs at ~6 s/pair/rank, so a "
|
| 147 |
+
"large candidate set will not fit a 4h interactive "
|
| 148 |
+
"window -- without this the job is SIGKILLed at the "
|
| 149 |
+
"wall clock and its afterok dependents are cancelled.")
|
| 150 |
ap.add_argument("--limit", type=int, default=0)
|
| 151 |
ap.add_argument("--shard", type=int, default=0)
|
| 152 |
ap.add_argument("--num_shards", type=int, default=1)
|
|
|
|
| 183 |
).eval()
|
| 184 |
|
| 185 |
kept = 0
|
| 186 |
+
t0 = time.monotonic()
|
| 187 |
with open(args.out, "w") as f:
|
| 188 |
for i, c in enumerate(cands):
|
| 189 |
+
if args.deadline_min and (time.monotonic() - t0) / 60 > args.deadline_min:
|
| 190 |
+
print(f"[deadline] stopping at {i}/{len(cands)} judged", flush=True)
|
| 191 |
+
break
|
| 192 |
try:
|
| 193 |
rf = sample_frames(c["ref_video"], args.num_frames)
|
| 194 |
tf = sample_frames(c["tgt_video"], args.num_frames)
|
viper/data/filter_clips.py
CHANGED
|
@@ -74,6 +74,49 @@ def detect_shot_transitions(path: str, thresh: float = 0.45, stride: int = 2) ->
|
|
| 74 |
return cuts
|
| 75 |
|
| 76 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 77 |
def main():
|
| 78 |
ap = argparse.ArgumentParser()
|
| 79 |
ap.add_argument("--video_dir", default="data/videos")
|
|
@@ -82,49 +125,35 @@ def main():
|
|
| 82 |
ap.add_argument("--min_motion", type=float, default=1.0)
|
| 83 |
ap.add_argument("--max_text_ratio", type=float, default=0.01)
|
| 84 |
ap.add_argument("--max_cuts", type=int, default=0)
|
|
|
|
|
|
|
| 85 |
args = ap.parse_args()
|
| 86 |
|
| 87 |
vdir = Path(args.video_dir)
|
| 88 |
metas = sorted(vdir.glob("*.json"))
|
| 89 |
-
kept, stats = [], {"total":
|
| 90 |
-
"
|
| 91 |
-
|
| 92 |
-
|
| 93 |
-
|
| 94 |
-
|
| 95 |
-
|
| 96 |
-
|
| 97 |
-
|
| 98 |
-
|
| 99 |
-
|
| 100 |
-
|
| 101 |
-
|
| 102 |
-
|
| 103 |
-
|
| 104 |
-
|
| 105 |
-
|
| 106 |
-
|
| 107 |
-
|
| 108 |
-
|
| 109 |
-
stats[
|
| 110 |
-
|
| 111 |
-
|
| 112 |
-
cuts = detect_shot_transitions(str(mp4))
|
| 113 |
-
if cuts > args.max_cuts:
|
| 114 |
-
stats["cuts"] += 1
|
| 115 |
-
continue
|
| 116 |
-
|
| 117 |
-
caption = mp.with_suffix(".txt")
|
| 118 |
-
kept.append({
|
| 119 |
-
"id": mp.stem,
|
| 120 |
-
"video": str(mp4),
|
| 121 |
-
"label": m["label"],
|
| 122 |
-
"caption": caption.read_text().strip() if caption.exists() else "",
|
| 123 |
-
"phys_law": m.get("physical_annotation", {}).get("phys_law", ""),
|
| 124 |
-
"motion_score": m.get("motion_score"),
|
| 125 |
-
"visual_quality_score": m.get("visual_quality_score"),
|
| 126 |
-
})
|
| 127 |
-
stats["kept"] += 1
|
| 128 |
|
| 129 |
os.makedirs(Path(args.out).parent, exist_ok=True)
|
| 130 |
with open(args.out, "w") as f:
|
|
|
|
| 74 |
return cuts
|
| 75 |
|
| 76 |
|
| 77 |
+
def check_clip(mp: Path, args) -> tuple[str, dict | None]:
|
| 78 |
+
"""Run every filter on one clip. Returns (reason, row); row is None if cut."""
|
| 79 |
+
m = json.loads(mp.read_text())
|
| 80 |
+
mp4 = mp.with_suffix(".mp4")
|
| 81 |
+
if not mp4.exists():
|
| 82 |
+
return "missing", None
|
| 83 |
+
|
| 84 |
+
if m.get("label") not in PHYSICAL_EVENT_LABELS:
|
| 85 |
+
return "label", None
|
| 86 |
+
if float(m.get("visual_quality_score", 0)) < args.min_quality:
|
| 87 |
+
return "quality", None
|
| 88 |
+
if float(m.get("motion_score", 0)) < args.min_motion:
|
| 89 |
+
return "motion", None
|
| 90 |
+
if float(m.get("text_bbox_ratio", 0)) > args.max_text_ratio:
|
| 91 |
+
return "text", None
|
| 92 |
+
|
| 93 |
+
# Decoding is the expensive part, so it runs last -- only on clips that
|
| 94 |
+
# already passed every metadata check.
|
| 95 |
+
try:
|
| 96 |
+
cuts = detect_shot_transitions(str(mp4))
|
| 97 |
+
except Exception:
|
| 98 |
+
return "decode", None
|
| 99 |
+
if cuts > args.max_cuts:
|
| 100 |
+
return "cuts", None
|
| 101 |
+
|
| 102 |
+
caption = mp.with_suffix(".txt")
|
| 103 |
+
return "kept", {
|
| 104 |
+
"id": mp.stem,
|
| 105 |
+
"video": str(mp4),
|
| 106 |
+
"label": m["label"],
|
| 107 |
+
"caption": caption.read_text().strip() if caption.exists() else "",
|
| 108 |
+
"phys_law": m.get("physical_annotation", {}).get("phys_law", ""),
|
| 109 |
+
"motion_score": m.get("motion_score"),
|
| 110 |
+
"visual_quality_score": m.get("visual_quality_score"),
|
| 111 |
+
}
|
| 112 |
+
|
| 113 |
+
|
| 114 |
+
def _check(pair):
|
| 115 |
+
"""Top-level wrapper so ProcessPoolExecutor can pickle the work item."""
|
| 116 |
+
mp, args = pair
|
| 117 |
+
return check_clip(mp, args)
|
| 118 |
+
|
| 119 |
+
|
| 120 |
def main():
|
| 121 |
ap = argparse.ArgumentParser()
|
| 122 |
ap.add_argument("--video_dir", default="data/videos")
|
|
|
|
| 125 |
ap.add_argument("--min_motion", type=float, default=1.0)
|
| 126 |
ap.add_argument("--max_text_ratio", type=float, default=0.01)
|
| 127 |
ap.add_argument("--max_cuts", type=int, default=0)
|
| 128 |
+
ap.add_argument("--workers", type=int, default=1,
|
| 129 |
+
help="processes for the decode-bound cut detector")
|
| 130 |
args = ap.parse_args()
|
| 131 |
|
| 132 |
vdir = Path(args.video_dir)
|
| 133 |
metas = sorted(vdir.glob("*.json"))
|
| 134 |
+
kept, stats = [], {"total": len(metas), "missing": 0, "label": 0,
|
| 135 |
+
"quality": 0, "motion": 0, "text": 0, "decode": 0,
|
| 136 |
+
"cuts": 0, "kept": 0}
|
| 137 |
+
|
| 138 |
+
if args.workers > 1:
|
| 139 |
+
import cv2
|
| 140 |
+
from concurrent.futures import ProcessPoolExecutor
|
| 141 |
+
|
| 142 |
+
cv2.setNumThreads(1) # avoid oversubscribing the node
|
| 143 |
+
with ProcessPoolExecutor(args.workers) as ex:
|
| 144 |
+
results = ex.map(_check, ((mp, args) for mp in metas), chunksize=16)
|
| 145 |
+
for i, (reason, row) in enumerate(results):
|
| 146 |
+
stats[reason] += 1
|
| 147 |
+
if row is not None:
|
| 148 |
+
kept.append(row)
|
| 149 |
+
if i % 2000 == 0:
|
| 150 |
+
print(f"{i}/{len(metas)} kept={len(kept)}", flush=True)
|
| 151 |
+
else:
|
| 152 |
+
for mp in metas:
|
| 153 |
+
reason, row = check_clip(mp, args)
|
| 154 |
+
stats[reason] += 1
|
| 155 |
+
if row is not None:
|
| 156 |
+
kept.append(row)
|
|
|
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|
| 157 |
|
| 158 |
os.makedirs(Path(args.out).parent, exist_ok=True)
|
| 159 |
with open(args.out, "w") as f:
|