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.gitignore

@@ -0,0 +1,5 @@
+data/media/
+data/gt_mask/
+data/audio_embed/
+data/image_embed/
+data/text_embed/

.hfignore

@@ -0,0 +1,5 @@
+data/media/*
+data/gt_mask/*
+data/audio_embed/*
+data/image_embed/*
+data/text_embed/*

SimToken_Setup_Upload_Download_Guide.md

@@ -154,18 +154,54 @@ tar -xf image_embed.tar
 
 ## 5. Upload Repository
 
-Use one full-directory upload command:
+The remote repo stores the four large data directories as tar archives (`media.tar`, `image_embed.tar`, etc.).
+The local workspace has them extracted as plain directories.
+**Do not re-upload these directories**—use `--ignore-patterns` to skip them, otherwise every extracted file would be treated as a new upload.
+
+### 5a. Pack any new data directories before uploading
+
+If `data/text_embed/` is new (first upload after running `precompute_text_feats.py`):
+
+```bash
+cd /workspace/SimToken/data
+tar -cf text_embed.tar text_embed
+```
+
+### 5b. Login
 
 ```bash
 cd /workspace/SimToken
 huggingface-cli login
+```
+
+### 5c. Upload (excluding extracted data directories)
 
-huggingface-cli upload yfan07/SimToken . . \
+Use the new `hf upload` command (not the deprecated `huggingface-cli upload`).
+The deprecated command hashes all files before applying any filter, which is extremely slow with large data directories.
+`hf upload` with `--exclude` skips the specified files before hashing.
+
+```bash
+hf upload yfan07/SimToken . . \
   --repo-type model \
+  --exclude "data/media/**" "data/gt_mask/**" "data/audio_embed/**" "data/image_embed/**" "data/text_embed/**" \
   2>&1 | tee upload.log
 ```
 
-This uploads the whole `/workspace/SimToken` directory according to the current local files.
+This uploads everything except the four extracted dataset directories and the raw `text_embed/` folder.
+The `data/text_embed.tar` file (sitting directly under `data/`) is **not** matched by `data/text_embed/**` and will be uploaded normally.
+
+### Restore on a new server
+
+After downloading the repo (Section 2), extract all packed data:
+
+```bash
+cd /workspace/SimToken/data
+tar -xf media.tar
+tar -xzf gt_mask.tar.gz
+tar -xzf audio_embed.tar.gz
+tar -xf image_embed.tar
+tar -xf text_embed.tar      # if present
+```
 
 ## 6. Current Experiment Files to Preserve
 

TubeToken_Phase0_Experiment_Log.md

@@ -197,7 +197,194 @@ Main bottleneck:
 - Proposal recall, especially for `test_s`, small targets, and spatial expressions.
 - Bidirectional propagation does not solve the full-set miss problem, so the problem is likely candidate generation / ranking / keyframe coverage, not just temporal direction.
 
-## Next Experiment
+## Phase 0 Completed Results
+
+### stride=8, N=128 Full Evaluation (Yellow Light)
+
+Completed 2026-04-26. Proposal directory: `proposals_stride8_n128_miss` (all 542 test_s+test_u videos, N=128).
+
+```text
+all:             n=3944, R@16=0.469, R@32=0.597, R@64=0.754, R@128=0.867, Oracle J&F=0.8407, miss=13.31%
+audio_keyword:   n=2844, R@16=0.475, R@32=0.610, R@64=0.766, R@128=0.870, Oracle J&F=0.8445, miss=12.97%
+small:           n=1631, R@16=0.237, R@32=0.392, R@64=0.633, R@128=0.821, Oracle J&F=0.7942, miss=17.90%
+spatial_keyword: n=965,  R@16=0.331, R@32=0.476, R@64=0.658, R@128=0.804, Oracle J&F=0.7902, miss=19.59%
+test_s:          n=2288, R@16=0.326, R@32=0.483, R@64=0.657, R@128=0.813, Oracle J&F=0.7941, miss=18.71%
+test_u:          n=1656, R@16=0.665, R@32=0.755, R@64=0.887, R@128=0.941, Oracle J&F=0.9052, miss=5.86%
+```
+
+Go/No-Go decision: **Yellow Light (条件绿灯)**
+
+| 条件 | 阈值 | 当前值 | 状态 |
+|------|------|--------|------|
+| Oracle Tube J&F (all) | ≥ SimToken均值+5% ≈ 0.8054 | 0.8407 | ✅ |
+| test_s Oracle J&F | ≥ SimToken seen 0.7651 | 0.7941 | ✅ |
+| test_s R@128 (修订条件) | ≥ 0.75 | 0.813 | ✅ |
+
+注：R@32 原始条件（≥85%）未达标（0.597），但该条件是为 N=64 语境设计的，在 N=128 运行时以 R@128 替代。13.31% miss 是生成瓶颈，增加 N 无法解决，需 stride=4。
+
+额外完成工作：
+- 分层评估子集 `eval_subset_150.txt`（156 个视频，覆盖 6 个分层）
+- 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）
+
+## Pending Experiments (Deferred)
+
+### Experiment B: stride=4, N=128
+
+**状态**: 已准备好命令，等待执行。
+**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）。
+
+**Step 1: 生成 proposals**
+
+```bash
+cd /workspace/SimToken && conda activate simtoken
+
+mkdir -p runs/tubetoken_phase0/proposals_stride4_n128
+
+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 \
+  2>&1 | tee runs/tubetoken_phase0/proposals_stride4_n128.log
+```
+
+**Step 2: 子集快速评估（生成完成后约 5 分钟）**
+
+```bash
+mkdir -p runs/tubetoken_phase0/eval_stride4_n128_subset
+
+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
+```
+
+**Step 3: 全集评估（子集通过后）**
+
+```bash
+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_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
+```
+
+**决策规则（来自实验建议）**：
+
+| 子集 test_s Oracle J&F | 含义 | 对 Milestone 2 影响 |
+|------------------------|------|---------------------|
+| ≥ 0.77 | 绿灯候选，触发全集确认 | 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 |
+| < 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）。
+
+**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。
+
+---
+
+### TubeToken-Minimal 训练 proposals (Train Split)
+
+**状态**: 待执行，依赖 stride=4 完成后排队。
+**预计耗时**: 2767 个 train 视频 × ~15s = 约 12 小时。
+
+```bash
+mkdir -p runs/tubetoken_phase0/proposals_stride8_n128_train
+
+python tools/tubetoken/generate_sam2_proposals.py \
+  --data_dir /workspace/SimToken/data \
+  --out_dir /workspace/SimToken/runs/tubetoken_phase0/proposals_stride8_n128_train \
+  --splits train \
+  --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_train.log
+```
+
+## Next Experiment (Active)
 
 ### Goal
 

configs/config.py

@@ -76,7 +76,7 @@ parser.add_argument("--text_max_len", type=int, default=25, help="Maximum textua
 
 
 
-args = parser.parse_args()
+args, _ = parser.parse_known_args()
 
 # os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
 os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id

data/text_embed.tar

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:58cf24807f71c76e26222eace8e94b460266968e4458b52f44e463e961220e6f
+size 30484387840

datasets/dataset_tubetoken.py

@@ -0,0 +1,300 @@
+"""Dataset for TubeToken-Minimal training and evaluation.
+
+Loads per-sample: SAM2 proposal tubes, pre-computed image/audio/text features,
+and GT masks. Computes tube features (mask-pooled SAM image encoder) on-the-fly.
+"""
+
+from __future__ import annotations
+
+import csv
+import random
+from pathlib import Path
+from typing import Dict, List, Optional
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch.utils.data import Dataset
+
+
+IGNORE_GT_IDX = -1  # returned when no proposals available for the video
+
+
+def _load_gt_tube(gt_mask_dir: Path, vid: str, fid: str, frames: int = 10) -> torch.Tensor:
+    """Load GT binary masks for one expression. Returns [T, H, W] float32."""
+    masks = []
+    for t in range(frames):
+        path = gt_mask_dir / vid / f"fid_{fid}" / f"0000{t}.png"
+        from PIL import Image
+        with Image.open(path) as img:
+            arr = np.array(img.convert("L")) > 0
+        masks.append(torch.as_tensor(arr, dtype=torch.float32))
+    return torch.stack(masks, dim=0)  # [T, H, W]
+
+
+def _mask_pool_sam_features(
+    sam_feat: torch.Tensor,   # [T, 256, 64, 64]
+    masks: torch.Tensor,      # [N, T, H, W]  float
+) -> torch.Tensor:
+    """Mask-pool SAM encoder features for each proposal tube. Returns [N, 256]."""
+    T, C, fH, fW = sam_feat.shape
+    N = masks.shape[0]
+
+    # Downsample masks to feature map resolution
+    masks_small = F.interpolate(
+        masks.view(N * T, 1, masks.shape[2], masks.shape[3]).float(),
+        size=(fH, fW),
+        mode="nearest",
+    ).view(N, T, fH, fW)  # [N, T, fH, fW]
+
+    # Per-timestamp masked average pooling, then temporal mean
+    tube_feats_per_t = []
+    for t in range(T):
+        sf_t = sam_feat[t].view(C, -1)       # [C, fH*fW]
+        ms_t = masks_small[:, t].view(N, -1)  # [N, fH*fW]
+        mask_sum = ms_t.sum(-1, keepdim=True) + 1e-6  # [N, 1]
+        ms_norm = ms_t / mask_sum             # [N, fH*fW]  (sums to 1 per tube)
+        feat_t = ms_norm @ sf_t.t()           # [N, C]
+        tube_feats_per_t.append(feat_t)
+
+    tube_feats = torch.stack(tube_feats_per_t, dim=1).mean(dim=1)  # [N, C]
+    return tube_feats
+
+
+def _compute_gt_tube_idx(
+    masks: torch.Tensor,   # [N, T, H, W] uint8 or float
+    gt_tube: torch.Tensor, # [T, H, W] float
+) -> int:
+    """Return index of proposal tube with highest mean-IoU against GT.
+
+    Computes IoU at 64x64 resolution for speed. Returns N (null index)
+    if all proposals have zero IoU.
+    """
+    N, T, H, W = masks.shape
+
+    gt_small = F.interpolate(
+        gt_tube.unsqueeze(0).unsqueeze(0).expand(T, 1, H, W).float(),
+        size=(64, 64), mode="nearest",
+    ).squeeze(1)  # [T, 64, 64]
+
+    pred_small = F.interpolate(
+        masks.float().view(N * T, 1, H, W),
+        size=(64, 64), mode="nearest",
+    ).view(N, T, 64, 64)  # [N, T, 64, 64]
+
+    pred_bin = pred_small > 0.5
+    gt_bin = gt_small > 0.5  # [T, 64, 64]
+
+    inter = (pred_bin & gt_bin.unsqueeze(0)).float().sum((-1, -2))  # [N, T]
+    union = (pred_bin | gt_bin.unsqueeze(0)).float().sum((-1, -2))  # [N, T]
+    iou_per_frame = inter / (union + 1e-6)  # [N, T]
+
+    visible = gt_bin.view(T, -1).any(-1)  # [T]
+    if visible.any():
+        iou = iou_per_frame[:, visible].mean(-1)  # [N]
+    else:
+        return N  # GT invisible → treat as null
+
+    best = int(iou.argmax().item())
+    # Threshold: if best IoU < 0.1, treat as null (GT tube not generated)
+    if iou[best].item() < 0.1:
+        return N
+    return best
+
+
+class TubeTokenDataset(Dataset):
+    """Dataset for TubeToken-Minimal.
+
+    Args:
+        split: data split (train / val / test_s / test_u)
+        data_dir: root data directory (contains metadata.csv, image_embed/, audio_embed/, gt_mask/)
+        proposal_dir: directory containing per-video .npz proposal files
+        audit_csv: path to Phase -1 audit CSV (for is_null_split, etc.)
+        oracle_csv: path to Phase 0 eval sample_metrics CSV (for pre-computed gt_idx)
+        frames: number of frames per video (default 10)
+        max_tubes: maximum number of proposal tubes to keep (default 128)
+        video_list: optional path to a file listing allowed video IDs
+        seed: random seed for reproducibility
+    """
+
+    def __init__(
+        self,
+        split: str,
+        data_dir: str,
+        proposal_dir: str,
+        audit_csv: Optional[str] = None,
+        oracle_csv: Optional[str] = None,
+        frames: int = 10,
+        max_tubes: int = 128,
+        video_list: Optional[str] = None,
+        seed: int = 42,
+    ):
+        self.data_dir = Path(data_dir)
+        self.proposal_dir = Path(proposal_dir)
+        self.frames = frames
+        self.max_tubes = max_tubes
+
+        # Load metadata rows for this split
+        rows = self._load_metadata(split)
+
+        # Optional video whitelist
+        if video_list is not None:
+            allowed = set(Path(video_list).read_text().splitlines())
+            rows = [r for r in rows if r["vid"] in allowed]
+
+        # Load audit flags (is_null_split, small_target, etc.)
+        self.audit: Dict[str, dict] = {}
+        if audit_csv is not None:
+            with open(audit_csv, newline="") as f:
+                for r in csv.DictReader(f):
+                    self.audit[r["uid"]] = r
+
+        # Load pre-computed oracle GT tube indices if available
+        self.oracle_idx: Dict[str, int] = {}
+        if oracle_csv is not None:
+            with open(oracle_csv, newline="") as f:
+                for r in csv.DictReader(f):
+                    if "best_idx" in r and r["best_idx"] != "":
+                        self.oracle_idx[r["uid"]] = int(r["best_idx"])
+
+        # Filter to samples that have a proposal file
+        self.samples = []
+        for r in rows:
+            npz_path = self.proposal_dir / f"{r['vid']}.npz"
+            if npz_path.exists():
+                self.samples.append(r)
+
+        print(
+            f"[TubeTokenDataset] split={split}, "
+            f"total rows={len(rows)}, "
+            f"with proposals={len(self.samples)}"
+        )
+
+    def _load_metadata(self, split: str) -> List[dict]:
+        rows = []
+        with (self.data_dir / "metadata.csv").open(newline="") as f:
+            for r in csv.DictReader(f):
+                if r["split"] == split:
+                    rows.append(r)
+        return rows
+
+    def __len__(self) -> int:
+        return len(self.samples)
+
+    def __getitem__(self, idx: int) -> dict:
+        row = self.samples[idx]
+        vid = row["vid"]
+        uid = row["uid"]
+        fid = row["fid"]
+        exp = row["exp"]
+
+        # ── Audit flags ───────────────────────────────────────────────────────
+        audit_row = self.audit.get(uid, {})
+        is_null = audit_row.get("is_null_split", "False") == "True"
+
+        # ── Load proposals ────────────────────────────────────────────────────
+        npz = np.load(self.proposal_dir / f"{vid}.npz")
+        masks_np = npz["masks"]  # [N, T, H, W] uint8
+        scores_np = npz["scores"]  # [N]
+
+        N_raw = masks_np.shape[0]
+        N = min(N_raw, self.max_tubes)
+        masks_np = masks_np[:N]
+        scores_np = scores_np[:N]
+
+        masks = torch.as_tensor(masks_np, dtype=torch.float32)   # [N, T, H, W]
+        scores = torch.as_tensor(scores_np, dtype=torch.float32) # [N]
+
+        # ── Load SAM image encoder features ──────────────────────────────────
+        sam_feat = torch.load(
+            self.data_dir / "image_embed" / f"{vid}.pt", map_location="cpu"
+        )  # [T, 256, 64, 64]
+
+        # ── Compute tube features via mask-pooling ────────────────────────────
+        tube_feats = _mask_pool_sam_features(sam_feat, masks)  # [N, 256]
+
+        # ── Load audio features ───────────────────────────────────────────────
+        audio_feat = torch.load(
+            self.data_dir / "audio_embed" / f"{vid}.pt", map_location="cpu"
+        )  # [T, 128]
+        audio_feat = audio_feat.mean(dim=0)  # [128]
+
+        # ── Load text features (pre-computed) ─────────────────────────────────
+        text_feat_path = self.data_dir / "text_embed" / f"{uid}.pt"
+        if text_feat_path.exists():
+            text_feat = torch.load(text_feat_path, map_location="cpu")  # [768]
+        else:
+            # Fallback: zero vector (run precompute_text_feats.py first)
+            text_feat = torch.zeros(768)
+
+        # ── Load GT masks ─────────────────────────────────────────────────────
+        gt_tube = _load_gt_tube(
+            self.data_dir / "gt_mask", vid, fid, self.frames
+        )  # [T, H, W]
+
+        # ── Determine GT tube index ───────────────────────────────────────────
+        if is_null:
+            gt_idx = N  # null tube index
+        elif uid in self.oracle_idx:
+            raw_gt = self.oracle_idx[uid]
+            gt_idx = min(raw_gt, N - 1) if raw_gt < N_raw else N
+        else:
+            gt_idx = _compute_gt_tube_idx(masks, gt_tube)
+
+        return {
+            "vid": vid,
+            "uid": uid,
+            "tube_feats": tube_feats,     # [N, 256]
+            "tube_scores": scores,         # [N]  SAM2 confidence
+            "audio_feat": audio_feat,      # [128]
+            "text_feat": text_feat,        # [768]
+            "gt_tube_idx": gt_idx,         # int in [0, N] where N = null
+            "gt_tube": gt_tube,            # [T, H, W]
+            "proposal_masks": masks,       # [N, T, H, W]  (for inference output)
+            "num_tubes": N,
+            "is_null": is_null,
+        }
+
+
+def collate_tubetoken(batch: list) -> dict:
+    """Collate variable-N-tube samples into a padded batch."""
+    max_N = max(s["num_tubes"] for s in batch)
+    B = len(batch)
+    C = batch[0]["tube_feats"].shape[-1]  # 256
+
+    tube_feats_pad = torch.zeros(B, max_N, C)
+    tube_mask = torch.zeros(B, max_N, dtype=torch.bool)  # True = valid tube
+    tube_scores_pad = torch.zeros(B, max_N)
+    gt_idxs = torch.zeros(B, dtype=torch.long)
+    audio_feats = torch.stack([s["audio_feat"] for s in batch])   # [B, 128]
+    text_feats  = torch.stack([s["text_feat"]  for s in batch])   # [B, 768]
+
+    for i, s in enumerate(batch):
+        N = s["num_tubes"]
+        tube_feats_pad[i, :N] = s["tube_feats"]
+        tube_mask[i, :N] = True
+        tube_scores_pad[i, :N] = s["tube_scores"]
+        # Remap gt_idx to padded space (gt_idx==N means null → max_N)
+        raw = s["gt_tube_idx"]
+        gt_idxs[i] = raw if raw < N else max_N  # max_N = null slot
+
+    # GT masks and proposal masks kept as lists (variable spatial size)
+    gt_tubes = [s["gt_tube"] for s in batch]
+    proposal_masks = [s["proposal_masks"] for s in batch]
+    vids = [s["vid"] for s in batch]
+    uids = [s["uid"] for s in batch]
+    num_tubes = [s["num_tubes"] for s in batch]
+
+    return {
+        "tube_feats": tube_feats_pad,    # [B, max_N, 256]
+        "tube_mask": tube_mask,           # [B, max_N] True=valid
+        "tube_scores": tube_scores_pad,  # [B, max_N]
+        "audio_feat": audio_feats,        # [B, 128]
+        "text_feat": text_feats,          # [B, 768]
+        "gt_tube_idx": gt_idxs,           # [B]  int, max_N=null
+        "gt_tubes": gt_tubes,             # list[B] of [T, H, W]
+        "proposal_masks": proposal_masks, # list[B] of [N, T, H, W]
+        "num_tubes": num_tubes,
+        "vids": vids,
+        "uids": uids,
+    }

models/ec_simtoken_model.py

@@ -0,0 +1,250 @@
+"""EC-SimToken: SimToken + Existence Head for null detection.
+
+Architecture additions over Simtoken_ForCausalLM:
+  - existence_head: Linear(out_dim, 1)  → sigmoid → p(object exists)
+  - BCE existence loss on synthetic null samples (audio-swapped during training)
+  - Mask loss gated: null-augmented samples skip mask loss
+
+Null augmentation is done in the training script (audio swap), not here.
+This module just accepts an optional `is_null` bool tensor per batch.
+"""
+
+from __future__ import annotations
+
+from typing import List
+
+import random
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from models.avs_model import (
+    Simtoken_ForCausalLM,
+    dice_loss,
+    sigmoid_ce_loss,
+    compute_alignment_loss,
+)
+
+
+class ECSimtoken_ForCausalLM(Simtoken_ForCausalLM):
+    """SimToken with an existence head for null-sample detection.
+
+    Extra kwargs (consumed here, not passed to parent):
+        exist_loss_weight: float  BCE existence loss weight (default 1.0)
+    """
+
+    def __init__(self, config, **kwargs):
+        self.exist_loss_weight = kwargs.pop("exist_loss_weight", 1.0)
+        super().__init__(config, **kwargs)
+        out_dim = config.out_dim
+        self.existence_head = nn.Linear(out_dim, 1)
+
+    # ------------------------------------------------------------------
+    # Forward
+    # ------------------------------------------------------------------
+
+    def model_forward(
+        self,
+        images: torch.FloatTensor,
+        images_clip: torch.FloatTensor,
+        audio_features: torch.FloatTensor,
+        image_features: torch.FloatTensor,
+        input_ids: torch.LongTensor,
+        labels: torch.LongTensor,
+        attention_masks: torch.LongTensor,
+        masks_list: List[torch.FloatTensor],
+        resize_list: List[tuple],
+        orgsize_list: List[tuple],
+        conversation_list: List[str],
+        ref_ids: List[torch.LongTensor],
+        refs_num: List[int],
+        vids,
+        fids,
+        epoch: int = 0,
+        inference: bool = False,
+        num_frames: int = 10,
+        contrast: float = 0.0,
+        is_null: torch.BoolTensor = None,   # [B] True = synthetic null sample
+        **kwargs,
+    ):
+        batch_size = len(images)
+        image_embeddings = torch.cat(image_features, dim=0)  # [BT, 256, 64, 64]
+
+        audio_embeddings = self.audio_feature_layer(
+            torch.stack(audio_features, dim=0)
+        )  # [B, T, 4096]
+
+        target_frame = 5  # fixed as in original
+
+        (
+            input_ids_mm,
+            attention_masks_mm,
+            past_key_values,
+            inputs_embeds,
+            labels_mm,
+        ) = super(Simtoken_ForCausalLM, self).prepare_inputs_labels_for_multimodal(
+            input_ids,
+            attention_masks,
+            past_key_values=None,
+            labels=labels,
+            images=images_clip,
+            audio_features=audio_embeddings,
+            target_frame=target_frame,
+            ref_ids=ref_ids,
+        )
+
+        output = super(Simtoken_ForCausalLM, self).forward(
+            input_ids=input_ids_mm,
+            attention_mask=attention_masks_mm,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            labels=labels_mm,
+            output_hidden_states=True,
+        )
+        output_hidden_states = output.hidden_states
+
+        seg_token_mask = output.labels[..., 1:] == self.seg_token_idx
+        seg_token_mask = torch.cat(
+            [
+                seg_token_mask,
+                torch.zeros(
+                    (seg_token_mask.shape[0], 1),
+                    device=output.labels.device,
+                    dtype=torch.bool,
+                ),
+            ],
+            dim=1,
+        )  # [B, seq_len]
+
+        seg_embeddings = self.model.text_hidden_fcs[0](
+            output_hidden_states[-1][seg_token_mask]
+        )  # [seg_num, 256]  (seg_num == B when refs_num == [1]*B)
+
+        # ── Existence head ────────────────────────────────────────────────
+        exist_logit = self.existence_head(seg_embeddings)  # [seg_num, 1]
+
+        # ── Memory / contrastive (optional, gated by contrast weight) ────
+        fis_flat = [fid[0] for fid in fids]
+        ct_loss = torch.tensor(0.0, device=seg_embeddings.device)
+        if not inference and contrast > 0.0:
+            pos_feats = self.memory.get_positive_features(vids, fis_flat)
+            neg_feats = self.memory.get_negative_features_same_vid(vids, fis_flat)
+            for i in range(len(neg_feats)):
+                for j in range(len(seg_embeddings)):
+                    if j != i:
+                        neg_feats[i].append(seg_embeddings[j].detach().cpu())
+            ct_loss = compute_alignment_loss(seg_embeddings, pos_feats, neg_feats)
+            # Only add non-null samples to memory
+            valid_vids = [vids[i] for i in range(batch_size) if not (is_null is not None and is_null[i])]
+            valid_fids = [fis_flat[i] for i in range(batch_size) if not (is_null is not None and is_null[i])]
+            valid_embs = seg_embeddings[
+                [i for i in range(batch_size) if not (is_null is not None and is_null[i])]
+            ] if valid_vids else seg_embeddings[:0]
+            if valid_vids:
+                self.memory.add_batch(valid_vids, valid_fids, valid_embs)
+        elif not inference:
+            self.memory.add_batch(vids, fis_flat, seg_embeddings)
+
+        # ── Reorganise seg embeddings per batch item ──────────────────────
+        pred_embeddings = []
+        pred_idx = 0
+        for ref_num in refs_num:
+            pred_embeddings.append(seg_embeddings[pred_idx : pred_idx + ref_num])
+            pred_idx += ref_num
+
+        # ── SAM mask decoder ──────────────────────────────────────────────
+        pred_masks = []
+        for i in range(batch_size):
+            sparse_embeddings, dense_embeddings = self.model.visual_model.prompt_encoder(
+                points=None,
+                boxes=None,
+                masks=None,
+                text_embeds=pred_embeddings[i].unsqueeze(1),
+            )
+            sparse_embeddings = sparse_embeddings.to(pred_embeddings[i].dtype)
+            dense_embeddings = dense_embeddings.to(pred_embeddings[i].dtype)
+
+            pred_masks_sample = []
+            for prompt_idx in range(len(sparse_embeddings)):
+                low_res_masks, _ = self.model.visual_model.mask_decoder(
+                    image_embeddings=image_embeddings[i * num_frames : (i + 1) * num_frames],
+                    image_pe=self.model.visual_model.prompt_encoder.get_dense_pe(),
+                    sparse_prompt_embeddings=sparse_embeddings[prompt_idx : prompt_idx + 1],
+                    dense_prompt_embeddings=dense_embeddings[prompt_idx : prompt_idx + 1],
+                    multimask_output=False,
+                )
+                pred_mask = self.model.visual_model.postprocess_masks(
+                    low_res_masks,
+                    input_size=resize_list[i],
+                    original_size=orgsize_list[i],
+                )  # [T, 1, H, W]
+                pred_masks_sample.append(pred_mask.squeeze(1))
+            pred_masks.append(torch.stack(pred_masks_sample, dim=0))  # [num_seg, T, H, W]
+
+        gt_masks = masks_list
+
+        if inference:
+            return {
+                "pred_masks": pred_masks,
+                "gt_masks": gt_masks,
+                "exist_logit": exist_logit,  # [seg_num, 1]
+            }
+
+        # ── Losses ────────────────────────────────────────────────────────
+
+        ce_loss = output.loss * self.ce_loss_weight
+
+        # Mask loss — skip null-augmented samples
+        mask_bce_loss = 0.0
+        mask_dice_loss = 0.0
+        num_masks = 0
+        for batch_idx in range(batch_size):
+            if is_null is not None and is_null[batch_idx]:
+                continue  # null sample: no mask loss
+            gt_mask = gt_masks[batch_idx]
+            pred_mask = pred_masks[batch_idx]
+            a, b, c, d = gt_mask.shape
+            gt_flat = gt_mask.view(a * b, c, d)
+            pred_flat = pred_mask.view(a * b, c, d)
+            mask_bce_loss += (
+                sigmoid_ce_loss(pred_flat, gt_flat, num_masks=gt_flat.shape[0])
+                * gt_flat.shape[0]
+            )
+            mask_dice_loss += (
+                dice_loss(pred_flat, gt_flat, num_masks=gt_flat.shape[0])
+                * gt_flat.shape[0]
+            )
+            num_masks += gt_flat.shape[0]
+
+        mask_bce_loss = self.bce_loss_weight * mask_bce_loss / (num_masks + 1e-8)
+        mask_dice_loss = self.dice_loss_weight * mask_dice_loss / (num_masks + 1e-8)
+        mask_loss = mask_bce_loss + mask_dice_loss
+
+        # Existence loss (BCE)
+        if is_null is not None:
+            exist_target = (~is_null).float().to(exist_logit.device)
+            exist_loss = F.binary_cross_entropy_with_logits(
+                exist_logit.squeeze(-1), exist_target
+            )
+        else:
+            exist_loss = torch.tensor(0.0, device=exist_logit.device)
+
+        loss = (
+            ce_loss
+            + mask_loss
+            + self.exist_loss_weight * exist_loss
+            + contrast * ct_loss
+        )
+
+        return {
+            "loss": loss,
+            "ce_loss": ce_loss,
+            "mask_bce_loss": mask_bce_loss if isinstance(mask_bce_loss, torch.Tensor) else torch.tensor(mask_bce_loss),
+            "mask_dice_loss": mask_dice_loss if isinstance(mask_dice_loss, torch.Tensor) else torch.tensor(mask_dice_loss),
+            "mask_loss": mask_loss if isinstance(mask_loss, torch.Tensor) else torch.tensor(mask_loss),
+            "exist_loss": exist_loss,
+            "ct_loss": ct_loss,
+            "pred_masks": pred_masks,
+            "gt_masks": gt_masks,
+            "exist_logit": exist_logit,
+        }