Add EasyCom encoder-only comparison workflow

README.md

@@ -11,6 +11,11 @@ The pooled local-feature B1 experiment (frozen Qwen face crops + recent audio,
 native 2048D sidecars, optional trainable projection, four-epoch ablations) is
 documented in [docs/local_feature_baseline.md](docs/local_feature_baseline.md).
 
+For **EasyCom encoder-only A / V / AV** with no Thinker states, start with
+[the EasyCom comparison commands](docs/easycom_encoder_only.md).
+They reuse the verified local sidecars, train on Session 4–12, test on 3/2/1,
+and keep four epochs with no dev set.
+
 The main experiment is deliberately simple:
 
 ```text
@@ -301,11 +306,13 @@ GPU workers, with per-GPU logs, validated resume, and foreground supervision.
 See the guide's L20 commands for the `/data1/yfzx/wangx` paths.
 
 For **encoder-only A / V / AV**, without Thinker states, see
-[the encoder-only comparison guide](docs/ego4d_encoder_only.md).
+[the Ego4D guide](docs/ego4d_encoder_only.md) or
+[the EasyCom-first guide](docs/easycom_encoder_only.md).
 `prepare_encoder_only_features.py` packs complete local vectors and validity
-bits on CPU; `run_ego4d_encoder_only.py` trains/evaluates four-epoch heads with
+bits on CPU (pass `--dataset easycom` for EasyCom; default remains Ego4D).
-no dev set. This native-concatenation baseline is not capacity- or
+`run_ego4d_encoder_only.py` / `run_easycom_encoder_only.py` train/evaluate
-context-matched to the Thinker baseline.
+four-epoch heads with no dev set. This native-concatenation baseline is not
+capacity- or context-matched to the Thinker baseline.
 
 ## Current Ego4D context-length results
 

docs/easycom_encoder_only.md

@@ -0,0 +1,143 @@
+# EasyCom：先跑 encoder-only A / V / AV 对照
+
+复用已经提取的 Qwen 局部编码器特征，不输入 Thinker hidden states，不重提音频或视频，
+不创建软链接。原 B1 的 `audio / visual / av` 分别是 **G+A / G+V / G+A+V**；
+这里的新结果命名为 `encoder_audio / encoder_visual / encoder_av`，分别只有 **A / V / AV**。
+
+固定训练 Session **4–12（升序，与原 B1 一致）**，测试 **3/2/1**；4 epochs、无 dev，
+评测最后一轮。沿用原来的 5Hz 行索引、十个 horizon、LSTR-MCE 和 AP 协议，不改变标签。
+
+## 1. 确定已经验证过的局部特征目录
+
+在 L20 机器更新本次代码后执行。下面从你之前跑完的 `av_seed0.pth` 读取实际路径，
+避免猜错目录或混入 bbox 修正前的旧视觉特征；checkpoint 仅用于找路径，不作为新模型输入。
+
+```bash
+cd /data1/yfzx/wangx/Code/icassp2027-experiment
+DATA_ROOT=/data1/yfzx/wangx/Datasets/easycom
+
+LOCAL_ROOT=$(python - <<'PY'
+from pathlib import Path
+import torch
+
+checkpoint = Path("checkpoints/easycom_local_b1_encoded_tf4576/av_seed0.pth")
+p = torch.load(checkpoint, map_location="cpu", weights_only=False)
+value = p["training_config"]["local_feature_root"]
+if not value:
+    raise ValueError("This checkpoint has no local-feature root")
+root = Path(value).expanduser().resolve()
+if not root.is_dir():
+    raise FileNotFoundError(root)
+print(root)
+PY
+) && printf 'LOCAL_ROOT=%s\n' "$LOCAL_ROOT"
+```
+
+如果 checkpoint 已移动，修改上述路径；也可以直接将 `LOCAL_ROOT` 设成以前
+`easycom_local_b1_encoded_tf4576` 实验实际使用的局部特征目录。
+必须包含完整的 `Session_1.pt` 到 `Session_12.pt`，不能用 `local_smoke_encoded`。
+下面命令对空变量和缺文件会报错，不会自动重提特征或换成 Thinker 输入。
+
+## 2. 先做 seed 0，三组前台顺序运行
+
+单张空闲 L20 足够先跑这组小 head，无需启动八卡提取或安装新的依赖。
+下面先 A、再 AV、最后 V；使用同一批冻结特征，仅改变输入模态。
+
+```bash
+(
+  set -euo pipefail
+  : "${DATA_ROOT:?Set DATA_ROOT first}"
+  : "${LOCAL_ROOT:?Set the verified local-feature directory first}"
+  export CUDA_VISIBLE_DEVICES=0
+  export OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4
+
+  for modality in audio av visual
+  do
+    ENCODER_ROOT="${DATA_ROOT}/omni_features/encoder_only_${modality}_1s"
+
+    python prepare_encoder_only_features.py \
+      --dataset easycom \
+      --data-root "$DATA_ROOT" \
+      --local-feature-root "$LOCAL_ROOT" \
+      --modalities "$modality" \
+      --output-root "$ENCODER_ROOT"
+
+    python run_easycom_encoder_only.py \
+      --data-root "$DATA_ROOT" \
+      --feature-root "$ENCODER_ROOT" \
+      --modalities "$modality" \
+      --seeds 0
+  done
+)
+```
+
+两条 Python 命令都支持 `--dry-run`。整理的 dry-run 只验证源文件，不生成训练输入；
+训练的 dry-run 则需要已完成的导出目录，只打印训练／评测命令，不启动训练。
+
+整理在 CPU 上完成：读取原生向量＋有效性标志，校验全部 12 sessions、时间/索引、
+配置一致性和 target 长度，逐文件保存，最后写出 `complete.txt`。
+默认 target 是 `$DATA_ROOT/easycom_target_frame`，可用 `--target-root` 覆盖。
+不使用 Ego4D manifest。新入口和 `--dataset easycom` 不影响已有 Ego4D 命令。
+
+输出：
+
+- 特征：`omni_features/encoder_only_audio_1s`、`encoder_only_av_1s`、`encoder_only_visual_1s`。
+- 模型：`checkpoints/easycom_encoder_only_1s/encoder_<modality>_seed0.pth`。
+- 评测：`results/easycom_encoder_only_1s/encoder_<modality>_seed0/metrics.json` 和 `pred/`。
+
+导出目录、checkpoint 或结果目录已存在时**拒绝覆盖**。不要重新跑整个循环来补 seeds；
+保留已完成的导出，只运行下面的训练入口。
+若某次导出中断、没有完成标记，请使用新的 `--output-root`，不要手动补标记。
+训练中断后换新的 `--checkpoint-root` / `--result-root`；不会自动恢复半成品训练。
+
+## 3. 打印结果，随后补 seed 1/2
+
+```bash
+python summarize_local_baselines.py \
+  --root results/easycom_encoder_only_1s \
+  --dataset easycom \
+  --protocol causal_valid_only
+```
+
+请贴出三行 `encoder_audio_seed0`、`encoder_av_seed0`、`encoder_visual_seed0`。
+原来的 81.72257 / 82.02661 / 73.47412 是 **G+A / G+A+V / G+V** 的 0.2s mAP，
+不能标为 encoder-only。比较时使用相同 seed 和同一 `causal_valid_only` 协议。
+
+第一轮确认能跑通后，补齐三个种子（不重新导出）：
+
+```bash
+(
+  set -euo pipefail
+  : "${DATA_ROOT:?Set DATA_ROOT first}"
+  export CUDA_VISIBLE_DEVICES=0
+  export OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4
+  for modality in audio av visual
+  do
+    python run_easycom_encoder_only.py \
+      --data-root "$DATA_ROOT" \
+      --feature-root "${DATA_ROOT}/omni_features/encoder_only_${modality}_1s" \
+      --modalities "$modality" \
+      --seeds 1 2
+  done
+)
+```
+
+## 实验能回答什么
+
+它测的是“Qwen 编码器近期 readout 本身的预测能力”，不是严格相同容量的 Thinker 消融。
+3B 的 A/V 原生各 2048D；带有效性位后单模态 2049D、AV 4098D，
+顺序是 `[visual, visual_valid, audio, audio_valid]`。不用 B1 的额外 256D 分支投影；
+沿用原 MLP 类，隐藏宽度为输入维度整除 2，因此单模态 1024、AV 2049。
+窗口、视觉 crop/视野和 head 参数量与原 Thinker 基线不同，应一起报告。
+
+batch=512、lr=7e-5、weight_decay=5e-5、warmup_epochs=1.6、warmup_factor=0.3、
+dropout=0.2、transition_objective=none。目录名 `1s` 不覆盖源文件的窗口配置，
+真实窗口保存在导出 `config.encoder_contract.config`；时间含义完全沿用源特征。
+
+本地 CPU 回归包括全部 12 sessions 的三模态导出、精确数值／时间对齐、缺文件和 partial 拒绝、
+固定 split 顺序、无 Thinker 依赖，以及三个模态各 4 epochs 的合成数据训练／推理。
+真实 EasyCom 精度和 L20 耗时由上述实验验证，不由这些测试推断。
+
+```bash
+python -m unittest -q test_easycom_encoder_only test_encoder_only_workflow
+```

docs/ego4d_encoder_only.md

@@ -1,5 +1,8 @@
 # Ego4D：不输入 Thinker 状态的 encoder-only 对照
 
+先做 EasyCom 时请用 [EasyCom encoder-only 命令](easycom_encoder_only.md)。
+整理脚本现支持 `--dataset easycom`，默认仍为 Ego4D，本页已有命令不变。
+
 本实验复用已经提取完的 `local_qwen_1s/<uid>.pt`，仅将选定的 Qwen 编码器向量和
 有效性标志作为 anticipation MLP 的输入。**不读取全局 Thinker `.pt`，不运行 Qwen，
 不重新处理视频，不使用软链接。** 原来的 `run_ego4d_local_baselines.py` 跑的是

docs/local_feature_baseline.md

@@ -4,6 +4,10 @@
 不是已证明的改进，也不是 multi-speaker attention 模型。
 保留原 `[T,4,2048]` 特征；不重新提取全局 Thinker，不改标签和 AP 协议。
 
+若要比较**不输入 Thinker 的纯 A / V / AV**，使用
+[EasyCom encoder-only 实验](easycom_encoder_only.md)。本页的 `audio/visual/av`
+仍包含全局 G，不能将两种设置混为一组。
+
 ## 固定实验定义
 
 | 分支 | 冻结特征 | 更新／池化 | 训练时处理 |

prepare_encoder_only_features.py

@@ -1,6 +1,6 @@
-"""Pack Ego4D local Qwen encoder vectors as standalone head inputs on CPU.
+"""Pack EasyCom/Ego4D local Qwen encoder vectors as standalone head inputs on CPU.
 
-No model, raw media, Thinker states or symlink index is loaded. All manifest
+No model, raw media, Thinker states or symlink index is loaded. All fixed-split
 train/test sidecars are validated before creating a fresh output directory.
 """
 
@@ -16,14 +16,38 @@ import numpy as np
 import torch
 from tqdm.auto import tqdm
 
-from easycom_head import load_ego4d_manifest_split, resolve_feature_path
+from easycom_head import (
+    DATASET_NAMES, OFFICIAL_TEST_SESSIONS, OFFICIAL_TRAIN_SESSIONS,
+    load_ego4d_manifest_split, resolve_feature_path,
+)
 from local_turn_features import MODALITIES, load_local_features
 
 
-DEFAULT_DATA_ROOT = Path("/data1/yfzx/wangx/Datasets/ego4d")
+DEFAULT_DATA_ROOTS = {dataset: Path("/data1/yfzx/wangx/Datasets") / dataset for dataset in DATASET_NAMES}
+DEFAULT_DATA_ROOT = DEFAULT_DATA_ROOTS["ego4d"]  # Preserve the original Ego4D default/API.
 PACKING = "native-vectors-plus-validity-bits"
 
 
+def encoder_sample_split(dataset, manifest=None):
+    """Fixed benchmark membership; EasyCom order matches the original B1 run."""
+    if dataset == "easycom":
+        if manifest is not None:
+            raise ValueError("EasyCom uses fixed sessions, not an Ego4D manifest")
+        number = lambda name: int(name.split("_")[-1])
+        # B1 explicitly passes sessions 4..12. Preserve that concatenation
+        # order too, so seeded shuffling sees the same sample ordering.
+        train = sorted(OFFICIAL_TRAIN_SESSIONS, key=number)
+        test = list(OFFICIAL_TEST_SESSIONS)
+        return sorted(train + test, key=number), train, test
+    if dataset != "ego4d" or manifest is None:
+        raise ValueError("Ego4D requires its EgoSpeak manifest")
+    return tuple(load_ego4d_manifest_split(manifest, part) for part in ("all", "train", "test"))
+
+
+def sample_unit(dataset):
+    return "session" if dataset == "easycom" else "clip"
+
+
 def encoder_only_mode(modalities):
     if modalities not in MODALITIES:
         raise ValueError(f"Unknown encoder modalities: {modalities}")
@@ -32,7 +56,8 @@ def encoder_only_mode(modalities):
 
 def parse_args(argv=None):
     parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--data-root", type=Path, default=DEFAULT_DATA_ROOT)
+    parser.add_argument("--dataset", choices=DATASET_NAMES, default="ego4d")
+    parser.add_argument("--data-root", type=Path)
     parser.add_argument("--manifest", type=Path)
     parser.add_argument("--local-feature-root", type=Path)
     parser.add_argument("--target-root", type=Path)
@@ -41,18 +66,24 @@ def parse_args(argv=None):
     parser.add_argument("--dry-run", action="store_true", help="Validate all inputs; no output files or directory")
     parser.add_argument("--no-progress", action="store_true")
     args = parser.parse_args(argv)
-    args.manifest = args.manifest or args.data_root / "ego4d_egospeak.tsv"
+    args.data_root = args.data_root or DEFAULT_DATA_ROOTS[args.dataset]
+    if args.dataset == "ego4d":
+        args.manifest = args.manifest or args.data_root / "ego4d_egospeak.tsv"
+    elif args.manifest is not None:
+        parser.error("--manifest is only supported for Ego4D; EasyCom uses all 12 fixed sessions")
     args.local_feature_root = args.local_feature_root or args.data_root / "omni_features/local_qwen_1s"
-    args.target_root = args.target_root or args.data_root / "ego4d_target_frame"
+    args.target_root = args.target_root or args.data_root / f"{args.dataset}_target_frame"
     args.output_root = args.output_root or args.data_root / f"omni_features/encoder_only_{args.modalities}_1s"
     for key in ("data_root", "manifest", "local_feature_root", "target_root", "output_root"):
-        setattr(args, key, getattr(args, key).expanduser().resolve())
+        value = getattr(args, key)
+        if value is not None:
+            setattr(args, key, value.expanduser().resolve())
     if args.output_root == args.local_feature_root:
         parser.error("Encoder-only output must be separate from local source features")
     return args
 
 
-def pack_sample(path, target_path, sample_id, modalities, expected_contract=None):
+def pack_sample(path, target_path, sample_id, modalities, expected_contract=None, *, dataset="ego4d"):
     """Read only selected encoder vectors; even a sidecar's reference path is unused."""
     source = torch.load(path, map_location="cpu", weights_only=False)
     if not isinstance(source, dict):
@@ -63,7 +94,7 @@ def pack_sample(path, target_path, sample_id, modalities, expected_contract=None
         "label_indices": source.get("label_indices"), "start": 0.,
     }
     states, contract = load_local_features(
-        path, reference, sample_id=sample_id, dataset="ego4d", modalities=modalities,
+        path, reference, sample_id=sample_id, dataset=dataset, modalities=modalities,
         expected_contract=expected_contract,
     )
     target = np.load(target_path, allow_pickle=False, mmap_mode="r")
@@ -75,7 +106,7 @@ def pack_sample(path, target_path, sample_id, modalities, expected_contract=None
     if not torch.isfinite(states).all():
         raise ValueError(f"{sample_id}: encoder vectors overflow bfloat16 storage")
     return {
-        "format": "qwen-encoder-only-v1", "dataset": "ego4d", "sample_id": sample_id,
+        "format": "qwen-encoder-only-v1", "dataset": dataset, "sample_id": sample_id,
         "states": states, "times": source["times"], "label_indices": source["label_indices"],
         "mode": encoder_only_mode(modalities), "start": 0., "end": len(states) / 5,
         "num_steps": len(states), "source_local_features": str(path),
@@ -88,10 +119,11 @@ def pack_sample(path, target_path, sample_id, modalities, expected_contract=None
 
 def main(argv=None):
     args = parse_args(argv)
-    names = load_ego4d_manifest_split(args.manifest, "all")
+    names, train, test = encoder_sample_split(args.dataset, args.manifest)
+    unit = sample_unit(args.dataset)
     if args.output_root.exists() and not args.dry_run:
         raise FileExistsError(f"Refusing to overwrite {args.output_root}; use a fresh output directory")
-    paths = {name: resolve_feature_path(args.local_feature_root, name, dataset="ego4d") for name in names}
+    paths = {name: resolve_feature_path(args.local_feature_root, name, dataset=args.dataset) for name in names}
     targets = {name: args.target_root / f"{name}.npy" for name in names}
     for path in targets.values():
         if not path.is_file():
@@ -99,19 +131,19 @@ def main(argv=None):
     contract, total, width = None, 0, None
     # Two passes keep memory bounded and prevent bad/partial test inputs from
     # leaving an apparently ready export. No full-dataset tensors are retained.
-    for name in tqdm(names, desc="encoder-only preflight", unit="clip", disable=args.no_progress):
+    for name in tqdm(names, desc="encoder-only preflight", unit=unit, disable=args.no_progress):
-        packed, contract = pack_sample(paths[name], targets[name], name, args.modalities, contract)
+        packed, contract = pack_sample(paths[name], targets[name], name, args.modalities, contract, dataset=args.dataset)
         total += len(packed["states"])
         width = packed["states"].shape[-1]
         del packed
-    print(f"Validated {len(names)} clips / {total} states; {encoder_only_mode(args.modalities)}; "
+    print(f"Validated {len(names)} {unit}s / {total} states; {encoder_only_mode(args.modalities)}; "
           f"input width={width}; no Thinker features", flush=True)
     if args.dry_run:
         return
     args.output_root.mkdir(parents=True, exist_ok=False)
     rows = {}
-    for name in tqdm(names, desc="pack encoder-only", unit="clip", disable=args.no_progress):
+    for name in tqdm(names, desc="pack encoder-only", unit=unit, disable=args.no_progress):
-        packed, _ = pack_sample(paths[name], targets[name], name, args.modalities, contract)
+        packed, _ = pack_sample(paths[name], targets[name], name, args.modalities, contract, dataset=args.dataset)
         destination = args.output_root / f"{name}.pt"
         with tempfile.NamedTemporaryFile(dir=args.output_root, prefix=f".{name}.", suffix=".tmp", delete=False) as handle:
             temporary = Path(handle.name)
@@ -123,13 +155,14 @@ def main(argv=None):
         rows[name] = len(packed["states"])
         del packed
     (args.output_root / "export_manifest.json").write_text(json.dumps({
-        "manifest": str(args.manifest), "mode": encoder_only_mode(args.modalities),
+        "dataset": args.dataset, "manifest": str(args.manifest) if args.manifest else None,
+        "train_samples": train, "test_samples": test, "mode": encoder_only_mode(args.modalities),
         "local_feature_root": str(args.local_feature_root), "target_root": str(args.target_root),
-        "encoder_contract": contract, "input_width": width, "num_states": total, "clips": rows,
+        "encoder_contract": contract, "input_width": width, "num_states": total, f"{unit}s": rows,
     }, indent=2) + "\n")
     # Written last. The runner also validates every train/test input instead of
     # trusting this marker alone. Compatible with the original inline command.
-    (args.output_root / "complete.txt").write_text(f"{len(names)} clips\n")
+    (args.output_root / "complete.txt").write_text(f"{len(names)} {unit}s\n")
     print(f"Complete: {args.output_root}", flush=True)
 
 

run_easycom_encoder_only.py

@@ -0,0 +1,16 @@
+"""EasyCom encoder-only: train Session 4..12, test 3/2/1, four epochs, no dev."""
+
+import run_ego4d_encoder_only as workflow
+from run_ego4d_encoder_only import experiment_commands, preflight, run_paths
+
+
+def parse_args(argv=None):
+    return workflow.parse_args(argv, dataset="easycom")
+
+
+def main(argv=None):
+    return workflow.main(argv, dataset="easycom")
+
+
+if __name__ == "__main__":
+    main()

run_ego4d_encoder_only.py

@@ -1,7 +1,8 @@
-"""Run raw encoder-only Ego4D heads: four epochs, no dev, manifest splits.
+"""Run raw encoder-only heads: four epochs, no dev, fixed benchmark splits.
 
 This is not a capacity/context-matched Thinker ablation: native encoder vectors
 and validity flags are concatenated, so the existing MLP adapts to input width.
+This entry defaults to Ego4D; run_easycom_encoder_only.py shares the workflow.
 """
 
 from __future__ import annotations
@@ -15,32 +16,44 @@ import sys
 
 from tqdm.auto import tqdm
 
-from easycom_head import load_ego4d_manifest_split, load_session_data
+from easycom_head import DATASET_NAMES, load_session_data
 from local_turn_features import LOCAL_FORMAT, MODALITIES
-from prepare_encoder_only_features import DEFAULT_DATA_ROOT, PACKING, encoder_only_mode
+from prepare_encoder_only_features import (
+    DEFAULT_DATA_ROOTS, PACKING, encoder_only_mode, encoder_sample_split, sample_unit,
+)
 
 
 REPO = Path(__file__).resolve().parent
 
 
-def parse_args(argv=None):
+def parse_args(argv=None, *, dataset="ego4d"):
-    parser = argparse.ArgumentParser(description=__doc__)
+    if dataset not in DATASET_NAMES:
-    parser.add_argument("--data-root", type=Path, default=DEFAULT_DATA_ROOT)
+        raise ValueError(f"Unknown dataset: {dataset}")
+    parser = argparse.ArgumentParser(
+        description=f"{dataset}: raw encoder-only A/V/AV heads; four epochs, no dev, fixed train/test splits."
+    )
+    parser.add_argument("--data-root", type=Path, default=DEFAULT_DATA_ROOTS[dataset])
     parser.add_argument("--manifest", type=Path)
     parser.add_argument("--target-root", type=Path)
     parser.add_argument("--feature-root", type=Path, required=True, help="Prepared encoder-only inputs, not Thinker/local sidecars")
     parser.add_argument("--modalities", choices=tuple(MODALITIES), default="av")
-    parser.add_argument("--checkpoint-root", type=Path, default=Path("checkpoints/ego4d_encoder_only_1s"))
+    parser.add_argument("--checkpoint-root", type=Path, default=Path(f"checkpoints/{dataset}_encoder_only_1s"))
-    parser.add_argument("--result-root", type=Path, default=Path("results/ego4d_encoder_only_1s"))
+    parser.add_argument("--result-root", type=Path, default=Path(f"results/{dataset}_encoder_only_1s"))
     parser.add_argument("--seeds", type=int, nargs="+", default=[0, 1, 2])
     parser.add_argument("--device", default="cuda:0")
     parser.add_argument("--dry-run", action="store_true", help="Validate all train/test inputs and print commands without training")
     parser.add_argument("--no-progress", action="store_true")
     args = parser.parse_args(argv)
-    args.manifest = args.manifest or args.data_root / "ego4d_egospeak.tsv"
+    args.dataset = dataset
-    args.target_root = args.target_root or args.data_root / "ego4d_target_frame"
+    if dataset == "ego4d":
+        args.manifest = args.manifest or args.data_root / "ego4d_egospeak.tsv"
+    elif args.manifest is not None:
+        parser.error("EasyCom uses train sessions 4..12 and test sessions 3/2/1, not --manifest")
+    args.target_root = args.target_root or args.data_root / f"{dataset}_target_frame"
     for key in ("data_root", "manifest", "target_root", "feature_root", "checkpoint_root", "result_root"):
-        setattr(args, key, getattr(args, key).expanduser().resolve())
+        value = getattr(args, key)
+        if value is not None:
+            setattr(args, key, value.expanduser().resolve())
     if len(set(args.seeds)) != len(args.seeds) or any(seed < 0 for seed in args.seeds):
         parser.error("Seeds must be unique and nonnegative")
     return args
@@ -52,19 +65,18 @@ def run_paths(args, seed):
 
 
 def preflight(args):
-    names = load_ego4d_manifest_split(args.manifest, "all")
+    names, train, test = encoder_sample_split(args.dataset, args.manifest)
-    train = load_ego4d_manifest_split(args.manifest, "train")
+    unit = sample_unit(args.dataset)
-    test = load_ego4d_manifest_split(args.manifest, "test")
     marker = args.feature_root / "complete.txt"
-    if not marker.is_file() or marker.read_text().strip() != f"{len(names)} clips":
+    if not marker.is_file() or marker.read_text().strip() != f"{len(names)} {unit}s":
         raise ValueError(f"Missing/incomplete encoder-only export: {marker}")
     for seed in args.seeds:
         checkpoint, result = run_paths(args, seed)
         if checkpoint.exists() or checkpoint.with_suffix(".pth.json").exists() or result.exists():
             raise FileExistsError(f"Refusing to overwrite {checkpoint} or {result}; choose unfinished seeds/new output roots")
     expected, total, width = None, 0, None
-    for name in tqdm(names, desc="encoder-only inputs", unit="clip", disable=args.no_progress):
+    for name in tqdm(names, desc="encoder-only inputs", unit=unit, disable=args.no_progress):
-        data = load_session_data(args.feature_root, args.target_root, name, dataset="ego4d",
+        data = load_session_data(args.feature_root, args.target_root, name, dataset=args.dataset,
                                  expected_mode=encoder_only_mode(args.modalities))
         config = data.feature_metadata.get("config") or {}
         contract = config.get("encoder_contract") or {}
@@ -83,20 +95,21 @@ def preflight(args):
         if data.states.ndim != 2 or data.hidden_size != width or len(data.states) <= 10:
             raise ValueError(f"{name}: invalid native encoder-vector/validity-bit shape")
         if expected is not None and config != expected:
-            raise ValueError(f"{name}: encoder export contract differs across clips")
+            raise ValueError(f"{name}: encoder export contract differs across {unit}s")
         expected = config
         total += len(data.states)
         del data
-    print(f"Encoder-only {args.modalities}: {len(train)} train / {len(test)} test; "
+    print(f"{args.dataset} encoder-only {args.modalities}: {len(train)} train / {len(test)} test; "
           f"{total} states, input width={width}, MLP hidden width={width // 2}; epochs=4, dev=none", flush=True)
     print("No Thinker features or local-fusion branch. Native concatenation changes head capacity; "
           "context remains the saved encoder windows.", flush=True)
 
 
 def experiment_commands(args):
-    common = ["--dataset", "ego4d", "--manifest", str(args.manifest),
+    common = ["--dataset", args.dataset, "--feature-root", str(args.feature_root), "--target-root", str(args.target_root),
-              "--feature-root", str(args.feature_root), "--target-root", str(args.target_root),
               "--expected-mode", encoder_only_mode(args.modalities), "--device", args.device]
+    if args.manifest is not None:
+        common += ["--manifest", str(args.manifest)]
     if args.no_progress:
         common.append("--no-progress")
     for seed in args.seeds:
@@ -108,11 +121,15 @@ def experiment_commands(args):
                  "--transition-objective", "none", "--seed", str(seed)]
         infer = [sys.executable, str(REPO / "infer_turn_head.py"), *common,
                  "--checkpoint", str(checkpoint), "--output-dir", str(result)]
+        if args.dataset == "easycom":
+            _, train_sessions, test_sessions = encoder_sample_split("easycom")
+            train += ["--sessions", *train_sessions]
+            infer += ["--sessions", *test_sessions]
         yield train, infer
 
 
-def main(argv=None):
+def main(argv=None, *, dataset="ego4d"):
-    args = parse_args(argv)
+    args = parse_args(argv, dataset=dataset)
     preflight(args)
     env = os.environ.copy()
     for name in ("OMP_NUM_THREADS", "MKL_NUM_THREADS", "OPENBLAS_NUM_THREADS"):
@@ -123,7 +140,7 @@ def main(argv=None):
             if not args.dry_run:
                 subprocess.run(command, cwd=REPO, env=env, check=True)
     summary = [sys.executable, str(REPO / "summarize_local_baselines.py"),
-               "--root", str(args.result_root), "--dataset", "ego4d"]
+               "--root", str(args.result_root), "--dataset", args.dataset]
     print(shlex.join(summary), flush=True)
     if not args.dry_run:
         subprocess.run(summary, cwd=REPO, env=env, check=True)

test_easycom_encoder_only.py

@@ -0,0 +1,216 @@
+"""EasyCom encoder-only split, packing and real CPU train/infer checks."""
+
+from __future__ import annotations
+
+import contextlib
+import copy
+import io
+import json
+import os
+from pathlib import Path
+import subprocess
+import sys
+import tempfile
+import unittest
+from unittest.mock import patch
+
+import numpy as np
+import torch
+
+from easycom_head import load_session_data
+from local_turn_features import MODALITIES
+import prepare_encoder_only_features as prepare
+import run_easycom_encoder_only as runner
+from test_local_turn_features import fixture
+
+
+class EasyComEncoderOnlyTests(unittest.TestCase):
+    def setUp(self):
+        torch.set_num_threads(1)
+        self.temp = tempfile.TemporaryDirectory()
+        self.addCleanup(self.temp.cleanup)
+        self.root = Path(self.temp.name)
+        self.names = [f"Session_{i}" for i in range(1, 13)]
+        self.train = self.names[3:]
+        self.test = ["Session_3", "Session_2", "Session_1"]
+        self.source = self.root / "local"
+        self.targets = self.root / "easycom_target_frame"
+        self.source.mkdir()
+        self.targets.mkdir()
+        self.payloads = {}
+        for name in self.names:
+            # No Thinker features, manifest, media, annotations or model exists.
+            _, side, target = fixture(name=name, dataset="easycom")
+            side["reference_features"] = "/must-not-be-read/thinker.pt"
+            side["states"] = "POISON: not an encoder input"
+            torch.save(side, self.source / f"{name}.pt")
+            np.save(self.targets / f"{name}.npy", target)
+            self.payloads[name] = side
+
+    def output_root(self, modality="av"):
+        return self.root / f"encoder_{modality}"
+
+    def prepare_args(self, modality="av", extra=()):
+        return ["--dataset", "easycom", "--data-root", str(self.root),
+                "--local-feature-root", str(self.source), "--modalities", modality,
+                "--output-root", str(self.output_root(modality)), "--no-progress", *extra]
+
+    def runner_args(self, modality="av", extra=()):
+        return ["--data-root", str(self.root), "--feature-root", str(self.output_root(modality)),
+                "--modalities", modality, "--checkpoint-root", str(self.root / "checkpoints"),
+                "--result-root", str(self.root / "results"), "--seeds", "0", "--device", "cpu",
+                "--no-progress", *extra]
+
+    def export(self, modality="av"):
+        with contextlib.redirect_stdout(io.StringIO()):
+            prepare.main(self.prepare_args(modality))
+
+    def test_dataset_defaults_and_ascending_b1_training_order(self):
+        args = prepare.parse_args(["--dataset", "easycom"])
+        root = Path("/data1/yfzx/wangx/Datasets/easycom")
+        self.assertEqual(args.data_root, root)
+        self.assertEqual(args.target_root, root / "easycom_target_frame")
+        self.assertIsNone(args.manifest)
+        run = runner.parse_args(["--feature-root", str(self.output_root())])
+        self.assertEqual(run.dataset, "easycom")
+        self.assertEqual(run.data_root, root)
+        self.assertEqual(run.target_root, root / "easycom_target_frame")
+        self.assertIsNone(run.manifest)
+        self.assertEqual(run.result_root.name, "easycom_encoder_only_1s")
+        self.assertEqual(run.checkpoint_root.name, "easycom_encoder_only_1s")
+        self.assertEqual(prepare.encoder_sample_split("easycom"), (self.names, self.train, self.test))
+        # Preserve the original export command's Ego4D default.
+        self.assertEqual(prepare.parse_args([]).dataset, "ego4d")
+
+    def test_all_modalities_export_twelve_real_files_and_exact_native_inputs(self):
+        for modality, parts in MODALITIES.items():
+            with self.subTest(modality=modality):
+                self.export(modality)
+                output = self.output_root(modality)
+                self.assertEqual((output / "complete.txt").read_text(), "12 sessions\n")
+                manifest = json.loads((output / "export_manifest.json").read_text())
+                self.assertEqual(manifest["dataset"], "easycom")
+                self.assertIsNone(manifest["manifest"])
+                self.assertEqual(manifest["train_samples"], self.train)
+                self.assertEqual(manifest["test_samples"], self.test)
+                self.assertEqual(manifest["sessions"], dict.fromkeys(self.names, 20))
+                self.assertEqual(manifest["num_states"], 240)
+                for name in self.names:
+                    path = output / f"{name}.pt"
+                    self.assertTrue(path.is_file())
+                    self.assertFalse(path.is_symlink())
+                    packed = torch.load(path, weights_only=False)
+                    original = self.payloads[name]
+                    expected = torch.cat([tensor for part in parts for tensor in (
+                        original[part].float(), original[f"{part}_valid"][:, None].float(),
+                    )], dim=-1).to(torch.bfloat16)
+                    self.assertTrue(torch.equal(packed["states"], expected))
+                    self.assertTrue(torch.equal(packed["times"], original["times"]))
+                    self.assertTrue(torch.equal(packed["label_indices"], original["label_indices"]))
+                    self.assertEqual(packed["dataset"], "easycom")
+                    self.assertIs(packed["config"]["uses_thinker_states"], False)
+                    data = load_session_data(output, self.targets, name,
+                                             expected_mode=f"encoder-only-{modality}")
+                    self.assertIsNone(data.local_features)
+                    self.assertEqual(tuple(data.states.shape), tuple(expected.shape))
+                with self.assertRaises(FileExistsError):
+                    prepare.main(self.prepare_args(modality))
+
+    def test_partial_wrong_dataset_and_missing_heldout_session_fail_before_write(self):
+        name = "Session_3"
+        path = self.source / f"{name}.pt"
+        for key, value in (("partial", True), ("dataset", "ego4d")):
+            side = copy.deepcopy(self.payloads[name])
+            side[key] = value
+            torch.save(side, path)
+            with self.subTest(key=key), self.assertRaises(ValueError):
+                prepare.main(self.prepare_args())
+            self.assertFalse(self.output_root().exists())
+        path.unlink()
+        with self.assertRaises(FileNotFoundError):
+            prepare.main(self.prepare_args())
+        self.assertFalse(self.output_root().exists())
+
+    def test_easycom_rejects_ego4d_manifest(self):
+        with contextlib.redirect_stderr(io.StringIO()), self.assertRaises(SystemExit):
+            prepare.parse_args(self.prepare_args(extra=["--manifest", "/ego4d.tsv"]))
+        with contextlib.redirect_stderr(io.StringIO()), self.assertRaises(SystemExit):
+            runner.parse_args(self.runner_args(extra=["--manifest", "/ego4d.tsv"]))
+
+    def test_dry_runs_write_nothing_and_use_fixed_sessions(self):
+        with contextlib.redirect_stdout(io.StringIO()):
+            prepare.main(self.prepare_args(extra=["--dry-run"]))
+        self.assertFalse(self.output_root().exists())
+        self.export()
+        args = runner.parse_args(self.runner_args(extra=["--seeds", "0", "1", "2"]))
+        commands = list(runner.experiment_commands(args))
+        self.assertEqual(len(commands), 3)
+        for seed, (train, infer) in enumerate(commands):
+            self.assertEqual(train[train.index("--epochs") + 1], "4")
+            self.assertEqual(train[train.index("--warmup-epochs") + 1], "1.6")
+            self.assertEqual(train[train.index("--seed") + 1], str(seed))
+            self.assertEqual(train[train.index("--sessions") + 1:], self.train)
+            self.assertEqual(infer[infer.index("--sessions") + 1:], self.test)
+            for command in (train, infer):
+                self.assertEqual(command[command.index("--dataset") + 1], "easycom")
+                self.assertEqual(command[command.index("--expected-mode") + 1], "encoder-only-av")
+                for forbidden in ("--manifest", "--local-feature-root", "--layer-fusion", "--dev-sessions"):
+                    self.assertNotIn(forbidden, command)
+        with patch.object(runner.workflow.subprocess, "run") as run, contextlib.redirect_stdout(io.StringIO()):
+            runner.main(self.runner_args(extra=["--dry-run"]))
+            run.assert_not_called()
+        self.assertFalse((self.root / "checkpoints").exists())
+
+    def test_runner_rejects_wrong_marker_missing_test_input_and_existing_results(self):
+        self.export()
+        args = runner.parse_args(self.runner_args())
+        marker = self.output_root() / "complete.txt"
+        marker.write_text("12 clips\n")
+        with self.assertRaisesRegex(ValueError, "incomplete"):
+            runner.preflight(args)
+        marker.write_text("12 sessions\n")
+        _, output = runner.run_paths(args, 0)
+        output.mkdir(parents=True)
+        with self.assertRaises(FileExistsError):
+            runner.preflight(args)
+        output.rmdir()
+        (self.output_root() / "Session_1.pt").unlink()
+        with self.assertRaises(FileNotFoundError):
+            runner.preflight(args)
+
+    def test_cpu_round_trip_three_modalities_four_epochs_no_dev(self):
+        env = os.environ.copy()
+        env.update(OMP_NUM_THREADS="1", MKL_NUM_THREADS="1", OPENBLAS_NUM_THREADS="1")
+        for modality, parts in MODALITIES.items():
+            with self.subTest(modality=modality):
+                self.export(modality)
+                result = subprocess.run(
+                    [sys.executable, str(Path(runner.__file__).resolve()), *self.runner_args(modality)],
+                    env=env, capture_output=True, text=True, timeout=120,
+                )
+                self.assertEqual(result.returncode, 0, result.stdout + result.stderr)
+                args = runner.parse_args(self.runner_args(modality))
+                checkpoint, output = runner.run_paths(args, 0)
+                saved = torch.load(checkpoint, map_location="cpu", weights_only=False)
+                self.assertEqual(saved["dataset"], "easycom")
+                self.assertEqual(saved["epoch"], 4)
+                self.assertEqual(saved["train_sessions"], self.train)
+                self.assertEqual(saved["dev_sessions"], [])
+                self.assertIsNone(saved["local_contract"])
+                self.assertIsNone(saved["training_config"]["local_feature_root"])
+                width = sum(self.payloads["Session_1"]["config"][f"{part}_dim"] + 1 for part in parts)
+                self.assertEqual(saved["head_config"]["hidden_size"], width)
+                self.assertEqual(saved["head_config"]["local_visual_dim"], 0)
+                self.assertEqual(saved["head_config"]["local_audio_dim"], 0)
+                self.assertEqual(saved["head_config"]["layer_fusion"], "single")
+                self.assertFalse(any("local_projections" in key for key in saved["head"]))
+                metrics = json.loads((output / "metrics.json").read_text())
+                self.assertEqual(metrics["sessions"], self.test)
+                self.assertEqual(metrics["checkpoint_epoch"], 4)
+                for name in self.test:
+                    self.assertEqual(np.load(output / "pred" / f"{name}.npy").shape, (20, 3, 10))
+                self.assertEqual({p.stem for p in (output / "pred").glob("*.npy")}, set(self.test))
+
+
+if __name__ == "__main__":
+    unittest.main()