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  1. .gitattributes +227 -0
  2. ChatUniVi/__pycache__/__init__.cpython-310.pyc +0 -0
  3. ChatUniVi/__pycache__/constants.cpython-310.pyc +0 -0
  4. ChatUniVi/model/__pycache__/__init__.cpython-310.pyc +0 -0
  5. ChatUniVi/model/__pycache__/arch.cpython-310.pyc +0 -0
  6. ChatUniVi/model/__pycache__/cluster.cpython-310.pyc +0 -0
  7. ChatUniVi/model/language_model/__pycache__/llama.cpython-310.pyc +0 -0
  8. ChatUniVi/model/multimodal_encoder/__pycache__/builder.cpython-310.pyc +0 -0
  9. ChatUniVi/model/multimodal_encoder/__pycache__/clip_encoder.cpython-310.pyc +0 -0
  10. ChatUniVi/model/multimodal_encoder/__pycache__/eva_encoder.cpython-310.pyc +0 -0
  11. ChatUniVi/model/multimodal_encoder/__pycache__/eva_vit.cpython-310.pyc +0 -0
  12. ChatUniVi/model/multimodal_encoder/__pycache__/processor.cpython-310.pyc +0 -0
  13. ChatUniVi/model/multimodal_encoder/__pycache__/utils.cpython-310.pyc +0 -0
  14. ChatUniVi/model/multimodal_projector/__pycache__/builder.cpython-310.pyc +0 -0
  15. SimToken_Setup_Upload_Download_Guide.md +0 -19
  16. TubeToken_Phase0_Experiment_Log.md +121 -142
  17. checkpoints/ec_simtoken/ec_simtoken_v1.pth +3 -0
  18. configs/__pycache__/__init__.cpython-310.pyc +0 -0
  19. configs/__pycache__/config.cpython-310.pyc +0 -0
  20. datasets/__pycache__/__init__.cpython-310.pyc +0 -0
  21. datasets/__pycache__/dataset_refavs.cpython-310.pyc +0 -0
  22. models/__pycache__/__init__.cpython-310.pyc +0 -0
  23. models/__pycache__/avs_model.cpython-310.pyc +0 -0
  24. models/__pycache__/ec_simtoken_model.cpython-310.pyc +0 -0
  25. models/llava/__pycache__/__init__.cpython-310.pyc +0 -0
  26. models/llava/__pycache__/conversation.cpython-310.pyc +0 -0
  27. models/llava/model/__pycache__/__init__.cpython-310.pyc +0 -0
  28. models/llava/model/__pycache__/llava_arch.cpython-310.pyc +0 -0
  29. models/llava/model/language_model/__pycache__/llava_llama.cpython-310.pyc +0 -0
  30. models/llava/model/language_model/__pycache__/llava_mpt.cpython-310.pyc +0 -0
  31. models/llava/model/language_model/mpt/__pycache__/adapt_tokenizer.cpython-310.pyc +0 -0
  32. models/llava/model/language_model/mpt/__pycache__/attention.cpython-310.pyc +0 -0
  33. models/llava/model/language_model/mpt/__pycache__/blocks.cpython-310.pyc +0 -0
  34. models/llava/model/language_model/mpt/__pycache__/configuration_mpt.cpython-310.pyc +0 -0
  35. models/llava/model/language_model/mpt/__pycache__/custom_embedding.cpython-310.pyc +0 -0
  36. models/llava/model/language_model/mpt/__pycache__/flash_attn_triton.cpython-310.pyc +0 -0
  37. models/llava/model/language_model/mpt/__pycache__/hf_prefixlm_converter.cpython-310.pyc +0 -0
  38. models/llava/model/language_model/mpt/__pycache__/meta_init_context.cpython-310.pyc +0 -0
  39. models/llava/model/language_model/mpt/__pycache__/modeling_mpt.cpython-310.pyc +0 -0
  40. models/llava/model/language_model/mpt/__pycache__/norm.cpython-310.pyc +0 -0
  41. models/llava/model/language_model/mpt/__pycache__/param_init_fns.cpython-310.pyc +0 -0
  42. models/llava/model/multimodal_encoder/__pycache__/builder.cpython-310.pyc +0 -0
  43. models/llava/model/multimodal_encoder/__pycache__/clip_encoder.cpython-310.pyc +0 -0
  44. models/segment_anything/__pycache__/__init__.cpython-310.pyc +0 -0
  45. models/segment_anything/__pycache__/automatic_mask_generator.cpython-310.pyc +0 -0
  46. models/segment_anything/__pycache__/build_sam.cpython-310.pyc +0 -0
  47. models/segment_anything/__pycache__/predictor.cpython-310.pyc +0 -0
  48. models/segment_anything/modeling/__pycache__/__init__.cpython-310.pyc +0 -0
  49. models/segment_anything/modeling/__pycache__/common.cpython-310.pyc +0 -0
  50. models/segment_anything/modeling/__pycache__/image_encoder.cpython-310.pyc +0 -0
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SimToken_Setup_Upload_Download_Guide.md CHANGED
@@ -58,25 +58,6 @@ huggingface-cli download yfan07/SimToken \
58
 
59
  ## 3. Model Preparation
60
 
61
- ### SAM for SimToken
62
-
63
- ```bash
64
- mkdir -p /workspace/SimToken/models/segment_anything
65
- cd /workspace/SimToken/models/segment_anything
66
-
67
- wget -O sam_vit_h_4b8939.pth \
68
- https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth
69
- ```
70
-
71
- ### SimToken Checkpoint
72
-
73
- ```bash
74
- mkdir -p /workspace/SimToken/checkpoints
75
-
76
- gdown 'https://drive.google.com/uc?id=1pargYfFy93rymCANuWV0nt6Lx3Ri406l' \
77
- -O /workspace/SimToken/checkpoints/simtoken_pretrained.pth
78
- ```
79
-
80
  ### Hugging Face Models
81
 
82
  ```bash
 
58
 
59
  ## 3. Model Preparation
60
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
61
  ### Hugging Face Models
62
 
63
  ```bash
TubeToken_Phase0_Experiment_Log.md CHANGED
@@ -227,32 +227,100 @@ Go/No-Go decision: **Yellow Light (条件绿灯)**
227
  - CLIP 文本特征预计算：`data/text_embed/`（19395 个文件，768-dim）
228
  - TubeToken-Minimal 框架骨架：`datasets/dataset_tubetoken.py`, `models/tubetoken_minimal.py`, `train_tubetoken.py`（smoke test 通过）
229
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
230
  ## Pending Experiments (Deferred)
231
 
232
  ### Experiment B: stride=4, N=128
233
 
234
- **状态**: 已准备好命令，等待执行。
235
  **目标**: 验证更密关键帧能否将 test_s miss% 从 18.71% 进一步降低。
236
- **预期耗时**: 全集生成约 4-5 小时（stride=8 花了 2 小时）。
 
237
 
238
- **Step 1: 生成 proposals**
239
 
240
  ```bash
241
- cd /workspace/SimToken && conda activate simtoken
242
-
243
- mkdir -p runs/tubetoken_phase0/proposals_stride4_n128
 
 
 
 
 
 
 
 
 
 
 
244
 
 
 
245
  python tools/tubetoken/generate_sam2_proposals.py \
246
- --data_dir /workspace/SimToken/data \
247
- --out_dir /workspace/SimToken/runs/tubetoken_phase0/proposals_stride4_n128 \
248
- --splits test_s,test_u \
249
- --sam2_repo /workspace/sam2 \
250
- --model_cfg configs/sam2.1/sam2.1_hiera_l.yaml \
251
  --checkpoint /workspace/sam2/checkpoints/sam2.1_hiera_large.pt \
252
  --stride 4 --max_tubes 128 \
253
  --device cuda --amp_dtype bf16 \
254
  --quiet_sam2 --no_group_by_keyframe \
255
- 2>&1 | tee runs/tubetoken_phase0/proposals_stride4_n128.log
 
256
  ```
257
 
258
  **Step 2: 子集快速评估（生成完成后约 5 分钟）**
@@ -294,72 +362,11 @@ python tools/tubetoken/evaluate_phase0_proposals.py \
294
  | 0.72–0.77 | 边际改善 | 保持 stride=8，N=128，不调整 |
295
  | < 0.72 | 生成瓶颈深于关键帧密度 | 保持 stride=8，N=128，不再追求绿灯 |
296
 
297
- ### EC-SimToken 实验
298
-
299
- **状态**: 代码已完成，等待运行。
300
- **目标**: 构建增强版 SimToken baseline，用于：
301
- 1. 为 TubeToken 提供竞争性对比基线（而非 weak baseline）
302
- 2. 验证音频交换合成 Null 增强策略的有效性
303
- 3. 为后续 CondQFormer 扩展提供基础
304
-
305
- **已完成工作**:
306
- - `models/ec_simtoken_model.py`: `ECSimtoken_ForCausalLM`，在 `Simtoken_ForCausalLM` 基础上添加 `existence_head = nn.Linear(256, 1)`；mask loss 对 null 样本 gate；BCE existence loss
307
- - `train_ec_simtoken.py`: 训练脚本，含音频交换 null 增强（`--null_aug_prob`）、bf16 加速、DataLoader `pin_memory`/`persistent_workers`
308
- - `configs/config.py`: 改为 `parse_known_args()` 以支持脚本扩展 args（已完成）
309
-
310
- **前置条件**: 有可用的 SimToken 预训练权重（`trained_simtoken.pth`），加载后 fine-tune。
311
-
312
- **Step 1: 训练（从 SimToken 权重初始化，fine-tune 5 个 epoch）**
313
-
314
- ```bash
315
- cd /workspace/SimToken && conda activate simtoken
316
-
317
- python train_ec_simtoken.py \
318
- --data_dir data \
319
- --mllm Chat-UniVi/Chat-UniVi-7B-v1.5 \
320
- --vision_pretrained path/to/sam_vit_h_4b8939.pth \
321
- --saved_model checkpoints/trained_simtoken.pth \
322
- --name ec_simtoken_v1 \
323
- --epochs 5 \
324
- --batch_size 12 \
325
- --null_aug_prob 0.25 \
326
- --exist_loss_weight 1.0 \
327
- --log_root runs/ec_simtoken \
328
- --checkpoint_root checkpoints/ec_simtoken \
329
- 2>&1 | tee runs/ec_simtoken/ec_simtoken_v1.log
330
- ```
331
-
332
- 预计耗时（bf16 + batch_size=12）：约 1.5–2h/epoch × 5 = 7.5–10h。
333
- 若显存不足，降至 `--batch_size 8`（约 2.5h/epoch）。
334
 
335
- **Step 2: 评估（seen/unseen/null 三个 split）**
336
-
337
- ```bash
338
- python train_ec_simtoken.py --run eval \
339
- --data_dir data \
340
- --mllm Chat-UniVi/Chat-UniVi-7B-v1.5 \
341
- --vision_pretrained path/to/sam_vit_h_4b8939.pth \
342
- --saved_model checkpoints/ec_simtoken/ec_simtoken_v1.pth \
343
- --eval_splits test_s,test_u,test_n \
344
- --exist_threshold 0.5
345
- ```
346
-
347
- **决策规则**:
348
-
349
- | 指标 | 阈值 | 含义 |
350
- |------|------|------|
351
- | test_s J&F | ≥ 0.7651（SimToken seen 基线） | 基础分割能力不退化 |
352
- | test_u J&F | ≥ 0.7456（SimToken unseen 基线） | 基础分割能力不退化 |
353
- | test_n Null_S | < 0.0100（SimToken 0.0118，越低越好） | Null 检测有效提升 |
354
- | test_n null_tp 率 | ≥ 70%（null_tp / (null_tp + null_fn)） | existence head 实际工作 |
355
-
356
- **调参方向**:
357
- - 若 test_s/test_u J&F 低于基线：降低 `--exist_loss_weight`（试 0.5），减少 existence loss 对分割分支的干扰
358
- - 若 null_tp 率 < 60%：增大 `--null_aug_prob`（试 0.35）或调低 `--exist_threshold`（试 0.4）
359
- - 若显存 OOM：`--batch_size 8`；若仍 OOM：`--batch_size 6`（回到原始配置）
360
-
361
- **与 TubeToken 的关系**:
362
- EC-SimToken 是论文对比基线，不是 TubeToken 开发的前置条件。可与 TubeToken-Minimal 并行执行。
363
 
364
  ---
365
 
@@ -386,86 +393,58 @@ python tools/tubetoken/generate_sam2_proposals.py \
386
 
387
  ## Next Experiment (Active)
388
 
389
- ### Goal
390
-
391
- Determine whether the red-light result is caused by top-64 truncation or by missing proposals at generation time.
392
-
393
- ### Step 1: Export R@64 Miss Video List
394
 
395
- Command:
396
 
397
- ```bash
398
- cd /workspace/SimToken
399
- conda activate simtoken
400
-
401
- python - <<'PY'
402
- import csv
403
- from pathlib import Path
404
 
405
- src = Path("runs/tubetoken_phase0/eval_stride8_n64_bidir/sample_metrics.csv")
406
- out = Path("runs/tubetoken_phase0/miss_videos_r64.txt")
407
-
408
- vids = set()
409
- with src.open() as f:
410
- for r in csv.DictReader(f):
411
- if r["recall@64"] != "True":
412
- vids.add(r["vid"])
413
-
414
- out.write_text("\n".join(sorted(vids)) + "\n")
415
- print("miss videos:", len(vids))
416
- print("wrote:", out)
417
- PY
418
- ```
419
-
420
- ### Step 2: Test N=128 on Miss Videos
421
-
422
- Command:
423
 
424
  ```bash
425
- mkdir -p runs/tubetoken_phase0/proposals_stride8_n128_miss
426
 
427
- python tools/tubetoken/generate_sam2_proposals.py \
428
- --data_dir /workspace/SimToken/data \
429
- --out_dir /workspace/SimToken/runs/tubetoken_phase0/proposals_stride8_n128_miss \
430
- --video_list /workspace/SimToken/runs/tubetoken_phase0/miss_videos_r64.txt \
431
- --splits test_s,test_u \
432
- --sam2_repo /workspace/sam2 \
433
- --model_cfg configs/sam2.1/sam2.1_hiera_l.yaml \
434
- --checkpoint /workspace/sam2/checkpoints/sam2.1_hiera_large.pt \
435
- --stride 8 \
436
- --max_tubes 128 \
437
- --device cuda \
438
- --amp_dtype bf16 \
439
- --quiet_sam2 \
440
- --no_group_by_keyframe \
441
- 2>&1 | tee runs/tubetoken_phase0/proposals_stride8_n128_miss.log
442
  ```
443
 
444
- Evaluate:
445
 
446
  ```bash
447
- mkdir -p runs/tubetoken_phase0/eval_stride8_n128_miss
448
 
449
  python tools/tubetoken/evaluate_phase0_proposals.py \
450
- --data_dir /workspace/SimToken/data \
451
- --proposal_dir /workspace/SimToken/runs/tubetoken_phase0/proposals_stride8_n128_miss \
452
- --out_dir /workspace/SimToken/runs/tubetoken_phase0/eval_stride8_n128_miss \
453
- --audit_csv /workspace/SimToken/runs/tubetoken_phase_minus1/audit_full/audit_samples.csv \
454
- --splits test_s,test_u \
455
- --video_list /workspace/SimToken/runs/tubetoken_phase0/miss_videos_r64.txt \
456
- --recall_ns 16,32,64,128 \
457
- 2>&1 | tee runs/tubetoken_phase0/eval_stride8_n128_miss.log
458
  ```
459
 
460
- Report:
461
 
462
- ```bash
463
- cat runs/tubetoken_phase0/eval_stride8_n128_miss/report.md
464
- ```
 
 
465
 
466
- Expected decision:
467
 
468
- - If `R@128` on miss videos improves strongly, run full `stride=8, N=128`.
469
- - If `R@128` remains low, candidate count is not the main issue; next test should increase keyframe coverage with `stride=4`.
470
- - If `stride=4` remains low, move to detector-assisted proposals or high-resolution proposal generation before TubeToken-Minimal.
 
 
 
 
471
 
 
227
  - CLIP 文本特征预计算：`data/text_embed/`（19395 个文件，768-dim）
228
  - TubeToken-Minimal 框架骨架：`datasets/dataset_tubetoken.py`, `models/tubetoken_minimal.py`, `train_tubetoken.py`（smoke test 通过）
229
 
230
+ ### EC-SimToken v1（已完成 — 诊断失败）
231
+
232
+ 完成于 2026-04-27。训练 5 epoch，batch_size=12，null_aug_prob=0.25，exist_loss_weight=1.0。
233
+ Checkpoint: `checkpoints/ec_simtoken/ec_simtoken_v1_ep5.pth`。
234
+
235
+ **分割指标（与 SimToken 基线对比）**
236
+
237
+ | Split | mIoU | F | J&F | SimToken J&F |
238
+ |-------|------|---|-----|--------------|
239
+ | test_s | 0.7062 | 0.8003 | 0.7533 | 0.7651 |
240
+ | test_u | 0.6855 | 0.7844 | 0.7350 | 0.7456 |
241
+
242
+ 分割能力略低于 SimToken（test_s -1.18pt，test_u -1.06pt），5 epoch fine-tune 未造成崩溃但未带来改善。
243
+
244
+ **Existence head 指标（ep5，threshold=0.50）**
245
+
246
+ ```text
247
+ ── p_exist distribution ─────────────────────────────────────
248
+ split n mean med p10 p25 p75 p90 min max
249
+ test_s(+) 2288 0.850 0.910 0.648 0.812 0.957 0.977 0.005 0.996
250
+ test_u(+) 1656 0.839 0.914 0.598 0.793 0.957 0.977 0.018 0.992
251
+ test_n(null) 1028 0.889 0.953 0.792 0.910 0.969 0.980 0.000 0.992
252
+
253
+ AUC-ROC (null vs positive): 0.3605
254
+ test_n null_tp=53/1028 (5.2%) Null_S=0.0100
255
+ ```
256
+
257
+ **Existence loss 轨迹**
258
+
259
+ | Epoch | mean exist_loss | 范围 |
260
+ |-------|----------------|------|
261
+ | 1 | 0.6218 | 0.82 → 0.54 |
262
+ | 2 | 0.3770 | 0.40 → 0.34 |
263
+ | 3 | 0.2860 | 0.33 → 0.27 |
264
+ | 4 | 0.2383 | 0.31 → 0.24 |
265
+ | 5 | 0.2351 | 0.24 → 0.23 |
266
+
267
+ **失败诊断**
268
+
269
+ exist_loss 确实收敛（0.82 → 0.23），说明 existence head 在训练集上学会了某个任务。但 AUC=0.36 < 0.5（随机），且 null 的 p_exist 均值（0.889）高于正样本（0.839-0.850），方向完全反转。
270
+
271
+ 根本原因：**训练-测试分布不匹配**。
272
+
273
+ | | 训练 null（合成） | test_n（真实） |
274
+ |---|---|---|
275
+ | 构造方式 | 随机 audio swap | 目标真实不在视频中 |
276
+ | 音频特征 | 与视频完全不匹配（随机） | 语义连贯，只是目标不可见 |
277
+ | 模型反应 | seg_embedding 混乱，head 可检测 | seg_embedding "自信但错误"，head 无法区分 |
278
+
279
+ existence head 学会了检测 audio-swap 造成的 embedding 异常，而非真实目标缺失。threshold sweep 无意义（分布顺序已反转）。
280
+
281
+ **决策**: EC-SimToken v1 定性为**诊断实验**，不作为论文主表强 baseline。不继续调参（调 threshold / loss weight / null_aug_prob 均无法修复分布错配）。保留 J&F 结果供参考，existence head 结论不对外声称有效。
282
+
283
  ## Pending Experiments (Deferred)
284
 
285
  ### Experiment B: stride=4, N=128
286
 
287
+ **状态**: **进行中（已中断，可续跑）**。已完成 227/542 个视频（41.9%），生成速度约 44s/video。
288
  **目标**: 验证更密关键帧能否将 test_s miss% 从 18.71% 进一步降低。
289
+ **Proposal 目录**: `runs/tubetoken_phase0/proposals_stride4_n128`（中断后 NPZ 文件保留，续跑自动跳过已完成视频）
290
+ **实际耗时**: stride=4 比 stride=8 慢约 3.4×（4 个 keyframe vs 3 个 + 更大 propagation state）。单进程全集约 6-7h；2-shard 并行约 3.5h。
291
 
292
+ **Step 1: 续跑生成 proposals（2-shard 并行，在两个终端同时启动）**
293
 
294
  ```bash
295
+ # Terminal 1 (shard 0)
296
+ cd /workspace/SimToken
297
+ python tools/tubetoken/generate_sam2_proposals.py \
298
+ --data_dir /workspace/SimToken/data \
299
+ --out_dir /workspace/SimToken/runs/tubetoken_phase0/proposals_stride4_n128 \
300
+ --splits test_s,test_u \
301
+ --sam2_repo /workspace/sam2 \
302
+ --model_cfg configs/sam2.1/sam2.1_hiera_l.yaml \
303
+ --checkpoint /workspace/sam2/checkpoints/sam2.1_hiera_large.pt \
304
+ --stride 4 --max_tubes 128 \
305
+ --device cuda --amp_dtype bf16 \
306
+ --quiet_sam2 --no_group_by_keyframe \
307
+ --num_shards 2 --shard_id 0 \
308
+ 2>&1 | tee runs/tubetoken_phase0/proposals_stride4_n128_s0.log
309
 
310
+ # Terminal 2 (shard 1)
311
+ cd /workspace/SimToken
312
  python tools/tubetoken/generate_sam2_proposals.py \
313
+ --data_dir /workspace/SimToken/data \
314
+ --out_dir /workspace/SimToken/runs/tubetoken_phase0/proposals_stride4_n128 \
315
+ --splits test_s,test_u \
316
+ --sam2_repo /workspace/sam2 \
317
+ --model_cfg configs/sam2.1/sam2.1_hiera_l.yaml \
318
  --checkpoint /workspace/sam2/checkpoints/sam2.1_hiera_large.pt \
319
  --stride 4 --max_tubes 128 \
320
  --device cuda --amp_dtype bf16 \
321
  --quiet_sam2 --no_group_by_keyframe \
322
+ --num_shards 2 --shard_id 1 \
323
+ 2>&1 | tee runs/tubetoken_phase0/proposals_stride4_n128_s1.log
324
  ```
325
 
326
  **Step 2: 子集快速评估（生成完成后约 5 分钟）**
 
362
  | 0.72–0.77 | 边际改善 | 保持 stride=8，N=128，不调整 |
363
  | < 0.72 | 生成瓶颈深于关键帧密度 | 保持 stride=8，N=128，不再追求绿灯 |
364
 
365
+ ### EC-SimToken v2（待设计）
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
366
 
367
+ **状态**: 暂缓。等待 Experiment B 完成后，视 TubeToken 主线进度再决定是否启动。
368
+ **前提**: v1 失败根因已定位（见下方 Phase 0 Completed Results），v2 需改用 in-distribution null 样本。
369
+ **方向**: cross-video query swap（同类别过滤）或直接使用 train_n split（如数据集提供）。
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
370
 
371
  ---
372
 
 
393
 
394
  ## Next Experiment (Active)
395
 
396
+ ### Experiment B: stride=4, N=128（续跑 + 评估）
 
 
 
 
397
 
398
+ **当前状态**: 227/542 NPZ 已完成，中断。续跑命令见 Pending Experiments → Experiment B。
399
 
400
+ **Step 1: 续跑生成**（见 Pending Experiments 中的 2-shard 命令，剩余约 315 个视频，2-shard 约 2-2.5h）
 
 
 
 
 
 
401
 
402
+ **Step 2: 子集评估（生成完成后，约 5 分钟）**
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
403
 
404
  ```bash
405
+ mkdir -p runs/tubetoken_phase0/eval_stride4_n128_subset
406
 
407
+ python tools/tubetoken/evaluate_phase0_proposals.py \
408
+ --data_dir /workspace/SimToken/data \
409
+ --proposal_dir /workspace/SimToken/runs/tubetoken_phase0/proposals_stride4_n128 \
410
+ --out_dir /workspace/SimToken/runs/tubetoken_phase0/eval_stride4_n128_subset \
411
+ --audit_csv /workspace/SimToken/runs/tubetoken_phase_minus1/audit_full/audit_samples.csv \
412
+ --splits test_s,test_u \
413
+ --video_list /workspace/SimToken/runs/tubetoken_phase0/eval_subset_150.txt \
414
+ --recall_ns 16,32,64,128 \
415
+ 2>&1 | tee runs/tubetoken_phase0/eval_stride4_n128_subset.log
 
 
 
 
 
 
416
  ```
417
 
418
+ **Step 3: 全集评估（子集 test_s Oracle J&F ≥ 0.77 时执行）**
419
 
420
  ```bash
421
+ mkdir -p runs/tubetoken_phase0/eval_stride4_n128_full
422
 
423
  python tools/tubetoken/evaluate_phase0_proposals.py \
424
+ --data_dir /workspace/SimToken/data \
425
+ --proposal_dir /workspace/SimToken/runs/tubetoken_phase0/proposals_stride4_n128 \
426
+ --out_dir /workspace/SimToken/runs/tubetoken_phase0/eval_stride4_n128_full \
427
+ --audit_csv /workspace/SimToken/runs/tubetoken_phase_minus1/audit_full/audit_samples.csv \
428
+ --splits test_s,test_u \
429
+ --recall_ns 16,32,64,128 \
430
+ 2>&1 | tee runs/tubetoken_phase0/eval_stride4_n128_full.log
 
431
  ```
432
 
433
+ **决策规则**
434
 
435
+ | 子集 test_s Oracle J&F | 结论 | 后续 |
436
+ |------------------------|------|------|
437
+ | ≥ 0.77 | 绿灯候选 | 跑全集；若全集通过，切换 backend 为 stride=4 |
438
+ | 0.72–0.77 | 边际改善 | 保持 stride=8 N=128，不调整 backend |
439
+ | < 0.72 | 关键帧密度不是主因 | 停止 stride 探索，TubeToken-Minimal 用 stride=8 |
440
 
441
+ **全集绿灯标准**（与 stride=8 对比）
442
 
443
+ | 指标 | stride=8 N=128 | 期望 stride=4 |
444
+ |------|----------------|---------------|
445
+ | test_s R@128 | 0.813 | 明显提升 |
446
+ | test_s miss% | 18.71% | 明显下降 |
447
+ | small R@128 | 0.821 | 提升 |
448
+ | all Oracle J&F | 0.8407 | 维持或提升 |
449
+ | test_s Oracle J&F | 0.7941 | 维持或提升 |
450
 
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