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ed1057e9e6294bb6ccfb64c7b6905022ae8e4870..04d0a267709f64e46b4af8034f1831021fc35608 100644 Binary files a/ChatUniVi/model/multimodal_projector/__pycache__/builder.cpython-310.pyc and b/ChatUniVi/model/multimodal_projector/__pycache__/builder.cpython-310.pyc differ diff --git a/SimToken_Setup_Upload_Download_Guide.md b/SimToken_Setup_Upload_Download_Guide.md index 3d8cf8a9ccc0d4ffe831bdb9aac97510ffdb751e..3f014a9b3a773b5e634ad6849f67603ec6ae089d 100644 --- a/SimToken_Setup_Upload_Download_Guide.md +++ b/SimToken_Setup_Upload_Download_Guide.md @@ -58,25 +58,6 @@ huggingface-cli download yfan07/SimToken \ ## 3. Model Preparation -### SAM for SimToken - -```bash -mkdir -p /workspace/SimToken/models/segment_anything -cd /workspace/SimToken/models/segment_anything - -wget -O sam_vit_h_4b8939.pth \ - https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth -``` - -### SimToken Checkpoint - -```bash -mkdir -p /workspace/SimToken/checkpoints - -gdown 'https://drive.google.com/uc?id=1pargYfFy93rymCANuWV0nt6Lx3Ri406l' \ - -O /workspace/SimToken/checkpoints/simtoken_pretrained.pth -``` - ### Hugging Face Models ```bash diff --git a/TubeToken_Phase0_Experiment_Log.md b/TubeToken_Phase0_Experiment_Log.md index 9613c1eb8b49ec62c46da0bd91556b4848025a3b..367eda9185f960dc950019cb7cea7fffc4dd93a0 100644 --- a/TubeToken_Phase0_Experiment_Log.md +++ b/TubeToken_Phase0_Experiment_Log.md @@ -227,32 +227,100 @@ Go/No-Go decision: **Yellow Light (条件绿灯)** - CLIP 文本特征预计算：`data/text_embed/`（19395 个文件，768-dim） - TubeToken-Minimal 框架骨架：`datasets/dataset_tubetoken.py`, `models/tubetoken_minimal.py`, `train_tubetoken.py`（smoke test 通过） +### EC-SimToken v1（已完成 — 诊断失败） + +完成于 2026-04-27。训练 5 epoch，batch_size=12，null_aug_prob=0.25，exist_loss_weight=1.0。 +Checkpoint: `checkpoints/ec_simtoken/ec_simtoken_v1_ep5.pth`。 + +**分割指标（与 SimToken 基线对比）** + +| Split | mIoU | F | J&F | SimToken J&F | +|-------|------|---|-----|--------------| +| test_s | 0.7062 | 0.8003 | 0.7533 | 0.7651 | +| test_u | 0.6855 | 0.7844 | 0.7350 | 0.7456 | + +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。 + +**Existence head 指标（ep5，threshold=0.50）** + +```text +── p_exist distribution ───────────────────────────────────── +split n mean med p10 p25 p75 p90 min max +test_s(+) 2288 0.850 0.910 0.648 0.812 0.957 0.977 0.005 0.996 +test_u(+) 1656 0.839 0.914 0.598 0.793 0.957 0.977 0.018 0.992 +test_n(null) 1028 0.889 0.953 0.792 0.910 0.969 0.980 0.000 0.992 + +AUC-ROC (null vs positive): 0.3605 +test_n null_tp=53/1028 (5.2%) Null_S=0.0100 +``` + +**Existence loss 轨迹** + +| Epoch | mean exist_loss | 范围 | +|-------|----------------|------| +| 1 | 0.6218 | 0.82 → 0.54 | +| 2 | 0.3770 | 0.40 → 0.34 | +| 3 | 0.2860 | 0.33 → 0.27 | +| 4 | 0.2383 | 0.31 → 0.24 | +| 5 | 0.2351 | 0.24 → 0.23 | + +**失败诊断** + +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。 + +根本原因：**训练-测试分布不匹配**。 + +| | 训练 null（合成） | test_n（真实） | +|---|---|---| +| 构造方式 | 随机 audio swap | 目标真实不在视频中 | +| 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 | +| 模型反应 | seg_embedding 混乱，head 可检测 | seg_embedding "自信但错误"，head 无法区分 | + +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）。 + +**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。 + ## Pending Experiments (Deferred) ### Experiment B: stride=4, N=128 -**状态**: 已准备好命令，等待执行。 +**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。 **目标**: 验证更密关键帧能否将 test_s miss% 从 18.71% 进一步降低。 -**预期耗时**: 全集生成约 4-5 小时（stride=8 花了 2 小时）。 +**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） +**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。 -**Step 1: 生成 proposals** +**Step 1: 续跑生成 proposals（2-shard 并行，在两个终端同时启动）** ```bash -cd /workspace/SimToken && conda activate simtoken - -mkdir -p runs/tubetoken_phase0/proposals_stride4_n128 +# Terminal 1 (shard 0) +cd /workspace/SimToken +python tools/tubetoken/generate_sam2_proposals.py \ + --data_dir /workspace/SimToken/data \ + --out_dir /workspace/SimToken/runs/tubetoken_phase0/proposals_stride4_n128 \ + --splits test_s,test_u \ + --sam2_repo /workspace/sam2 \ + --model_cfg configs/sam2.1/sam2.1_hiera_l.yaml \ + --checkpoint /workspace/sam2/checkpoints/sam2.1_hiera_large.pt \ + --stride 4 --max_tubes 128 \ + --device cuda --amp_dtype bf16 \ + --quiet_sam2 --no_group_by_keyframe \ + --num_shards 2 --shard_id 0 \ + 2>&1 | tee runs/tubetoken_phase0/proposals_stride4_n128_s0.log +# Terminal 2 (shard 1) +cd /workspace/SimToken python tools/tubetoken/generate_sam2_proposals.py \ - --data_dir /workspace/SimToken/data \ - --out_dir /workspace/SimToken/runs/tubetoken_phase0/proposals_stride4_n128 \ - --splits test_s,test_u \ - --sam2_repo /workspace/sam2 \ - --model_cfg configs/sam2.1/sam2.1_hiera_l.yaml \ + --data_dir /workspace/SimToken/data \ + --out_dir /workspace/SimToken/runs/tubetoken_phase0/proposals_stride4_n128 \ + --splits test_s,test_u \ + --sam2_repo /workspace/sam2 \ + --model_cfg configs/sam2.1/sam2.1_hiera_l.yaml \ --checkpoint /workspace/sam2/checkpoints/sam2.1_hiera_large.pt \ --stride 4 --max_tubes 128 \ --device cuda --amp_dtype bf16 \ --quiet_sam2 --no_group_by_keyframe \ - 2>&1 | tee runs/tubetoken_phase0/proposals_stride4_n128.log + --num_shards 2 --shard_id 1 \ + 2>&1 | tee runs/tubetoken_phase0/proposals_stride4_n128_s1.log ``` **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 \ | 0.72–0.77 | 边际改善 | 保持 stride=8，N=128，不调整 | | < 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 | -### EC-SimToken 实验 - -**状态**: 代码已完成，等待运行。 -**目标**: 构建增强版 SimToken baseline，用于： -1. 为 TubeToken 提供竞争性对比基线（而非 weak baseline） -2. 验证音频交换合成 Null 增强策略的有效性 -3. 为后续 CondQFormer 扩展提供基础 - -**已完成工作**: -- `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 -- `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` -- `configs/config.py`: 改为 `parse_known_args()` 以支持脚本扩展 args（已完成） - -**前置条件**: 有可用的 SimToken 预训练权重（`trained_simtoken.pth`），加载后 fine-tune。 - -**Step 1: 训练（从 SimToken 权重初始化，fine-tune 5 个 epoch）** - -```bash -cd /workspace/SimToken && conda activate simtoken - -python train_ec_simtoken.py \ - --data_dir data \ - --mllm Chat-UniVi/Chat-UniVi-7B-v1.5 \ - --vision_pretrained path/to/sam_vit_h_4b8939.pth \ - --saved_model checkpoints/trained_simtoken.pth \ - --name ec_simtoken_v1 \ - --epochs 5 \ - --batch_size 12 \ - --null_aug_prob 0.25 \ - --exist_loss_weight 1.0 \ - --log_root runs/ec_simtoken \ - --checkpoint_root checkpoints/ec_simtoken \ - 2>&1 | tee runs/ec_simtoken/ec_simtoken_v1.log -``` - -预计耗时（bf16 + batch_size=12）：约 1.5–2h/epoch × 5 = 7.5–10h。 -若显存不足，降至 `--batch_size 8`（约 2.5h/epoch）。 +### EC-SimToken v2（待设计） -**Step 2: 评估（seen/unseen/null 三个 split）** - -```bash -python train_ec_simtoken.py --run eval \ - --data_dir data \ - --mllm Chat-UniVi/Chat-UniVi-7B-v1.5 \ - --vision_pretrained path/to/sam_vit_h_4b8939.pth \ - --saved_model checkpoints/ec_simtoken/ec_simtoken_v1.pth \ - --eval_splits test_s,test_u,test_n \ - --exist_threshold 0.5 -``` - -**决策规则**: - -| 指标 | 阈值 | 含义 | -|------|------|------| -| test_s J&F | ≥ 0.7651（SimToken seen 基线） | 基础分割能力不退化 | -| test_u J&F | ≥ 0.7456（SimToken unseen 基线） | 基础分割能力不退化 | -| test_n Null_S | < 0.0100（SimToken 0.0118，越低越好） | Null 检测有效提升 | -| test_n null_tp 率 | ≥ 70%（null_tp / (null_tp + null_fn)） | existence head 实际工作 | - -**调参方向**: -- 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 -- 若 null_tp 率 < 60%：增大 `--null_aug_prob`（试 0.35）或调低 `--exist_threshold`（试 0.4） -- 若显存 OOM：`--batch_size 8`；若仍 OOM：`--batch_size 6`（回到原始配置） - -**与 TubeToken 的关系**: -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。 +**状态**: 暂缓。等待 Experiment B 完成后，视 TubeToken 主线进度再决定是否启动。 +**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。 +**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）。 --- @@ -386,86 +393,58 @@ python tools/tubetoken/generate_sam2_proposals.py \ ## Next Experiment (Active) -### Goal - -Determine whether the red-light result is caused by top-64 truncation or by missing proposals at generation time. - -### Step 1: Export R@64 Miss Video List +### Experiment B: stride=4, N=128（续跑 + 评估） -Command: +**当前状态**: 227/542 NPZ 已完成，中断。续跑命令见 Pending Experiments → Experiment B。 -```bash -cd /workspace/SimToken -conda activate simtoken - -python - <<'PY' -import csv -from pathlib import Path +**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） -src = Path("runs/tubetoken_phase0/eval_stride8_n64_bidir/sample_metrics.csv") -out = Path("runs/tubetoken_phase0/miss_videos_r64.txt") - -vids = set() -with src.open() as f: - for r in csv.DictReader(f): - if r["recall@64"] != "True": - vids.add(r["vid"]) - -out.write_text("\n".join(sorted(vids)) + "\n") -print("miss videos:", len(vids)) -print("wrote:", out) -PY -``` - -### Step 2: Test N=128 on Miss Videos - -Command: +**Step 2: 子集评估（生成完成后，约 5 分钟）** ```bash -mkdir -p runs/tubetoken_phase0/proposals_stride8_n128_miss +mkdir -p runs/tubetoken_phase0/eval_stride4_n128_subset -python tools/tubetoken/generate_sam2_proposals.py \ - --data_dir /workspace/SimToken/data \ - --out_dir /workspace/SimToken/runs/tubetoken_phase0/proposals_stride8_n128_miss \ - --video_list /workspace/SimToken/runs/tubetoken_phase0/miss_videos_r64.txt \ - --splits test_s,test_u \ - --sam2_repo /workspace/sam2 \ - --model_cfg configs/sam2.1/sam2.1_hiera_l.yaml \ - --checkpoint /workspace/sam2/checkpoints/sam2.1_hiera_large.pt \ - --stride 8 \ - --max_tubes 128 \ - --device cuda \ - --amp_dtype bf16 \ - --quiet_sam2 \ - --no_group_by_keyframe \ - 2>&1 | tee runs/tubetoken_phase0/proposals_stride8_n128_miss.log +python tools/tubetoken/evaluate_phase0_proposals.py \ + --data_dir /workspace/SimToken/data \ + --proposal_dir /workspace/SimToken/runs/tubetoken_phase0/proposals_stride4_n128 \ + --out_dir /workspace/SimToken/runs/tubetoken_phase0/eval_stride4_n128_subset \ + --audit_csv /workspace/SimToken/runs/tubetoken_phase_minus1/audit_full/audit_samples.csv \ + --splits test_s,test_u \ + --video_list /workspace/SimToken/runs/tubetoken_phase0/eval_subset_150.txt \ + --recall_ns 16,32,64,128 \ + 2>&1 | tee runs/tubetoken_phase0/eval_stride4_n128_subset.log ``` -Evaluate: +**Step 3: 全集评估（子集 test_s Oracle J&F ≥ 0.77 时执行）** ```bash -mkdir -p runs/tubetoken_phase0/eval_stride8_n128_miss +mkdir -p runs/tubetoken_phase0/eval_stride4_n128_full python tools/tubetoken/evaluate_phase0_proposals.py \ - --data_dir /workspace/SimToken/data \ - --proposal_dir /workspace/SimToken/runs/tubetoken_phase0/proposals_stride8_n128_miss \ - --out_dir /workspace/SimToken/runs/tubetoken_phase0/eval_stride8_n128_miss \ - --audit_csv /workspace/SimToken/runs/tubetoken_phase_minus1/audit_full/audit_samples.csv \ - --splits test_s,test_u \ - --video_list /workspace/SimToken/runs/tubetoken_phase0/miss_videos_r64.txt \ - --recall_ns 16,32,64,128 \ - 2>&1 | tee runs/tubetoken_phase0/eval_stride8_n128_miss.log + --data_dir /workspace/SimToken/data \ + --proposal_dir /workspace/SimToken/runs/tubetoken_phase0/proposals_stride4_n128 \ + --out_dir /workspace/SimToken/runs/tubetoken_phase0/eval_stride4_n128_full \ + --audit_csv /workspace/SimToken/runs/tubetoken_phase_minus1/audit_full/audit_samples.csv \ + --splits test_s,test_u \ + --recall_ns 16,32,64,128 \ + 2>&1 | tee runs/tubetoken_phase0/eval_stride4_n128_full.log ``` -Report: +**决策规则** -```bash -cat runs/tubetoken_phase0/eval_stride8_n128_miss/report.md -``` +| 子集 test_s Oracle J&F | 结论 | 后续 | +|------------------------|------|------| +| ≥ 0.77 | 绿灯候选 | 跑全集；若全集通过，切换 backend 为 stride=4 | +| 0.72–0.77 | 边际改善 | 保持 stride=8 N=128，不调整 backend | +| < 0.72 | 关键帧密度不是主因 | 停止 stride 探索，TubeToken-Minimal 用 stride=8 | -Expected decision: +**全集绿灯标准**（与 stride=8 对比） -- If `R@128` on miss videos improves strongly, run full `stride=8, N=128`. -- If `R@128` remains low, candidate count is not the main issue; next test should increase keyframe coverage with `stride=4`. -- If `stride=4` remains low, move to detector-assisted proposals or high-resolution proposal generation before TubeToken-Minimal. +| 指标 | stride=8 N=128 | 期望 stride=4 | 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