重命名 refs 为 algorithm_subprocesses，并清理误提交的权重与缓存文件。

算法子进程目录仅保留源码与配置；权重、样本 I/O、构建产物通过 .gitignore 离线交付。

Co-authored-by: Cursor <cursoragent@cursor.com>

.gitignore

@@ -56,6 +56,16 @@ backend/Ultralytics/
 # Large model weights (deploy via mount or offline delivery)
 backend/app/resources/actionformer_epoch_045.pth.tar
 
+# Algorithm subprocess bundles — runtime assets ignored via backend/algorithm_subprocesses/.gitignore
+backend/algorithm_subprocesses/**/weights/*
+!backend/algorithm_subprocesses/**/weights/.gitkeep
+backend/algorithm_subprocesses/**/input/*
+!backend/algorithm_subprocesses/**/input/.gitkeep
+backend/algorithm_subprocesses/**/output/*
+!backend/algorithm_subprocesses/**/output/.gitkeep
+backend/algorithm_subprocesses/**/data/*
+!backend/algorithm_subprocesses/**/data/.gitkeep
+
 # --- Clients ---
 clients/demo-client/.runtime/
 clients/demo-client/labels.json

backend/.dockerignore

@@ -10,6 +10,6 @@ __pycache__
 .env.*
 !.env.example
 !.env.prod
-refs
+algorithm_subprocesses
 *.md
 .dockerignore

backend/algorithm_subprocesses/.gitignore

@@ -0,0 +1,44 @@
+# 算法子进程包（algorithm subprocess bundles）
+# 源码与配置保留在 git；权重、样本 I/O、构建缓存离线交付或挂载。
+# 详见 algorithm_subprocesses/5.15/README.md §「准备权重与输入」。
+
+# --- 运行时 I/O（保留 .gitkeep）---
+**/weights/*
+!**/weights/.gitkeep
+**/input/*
+!**/input/.gitkeep
+**/output/*
+!**/output/.gitkeep
+**/data/*
+!**/data/.gitkeep
+
+# --- 大文件 / 离线资产 ---
+*.pt
+*.pth
+*.pth.tar
+*.pkl
+*.task
+*.mp4
+*.xlsx
+*.xls
+**/商品信息表.xlsx
+
+# --- 子包运行时 ---
+doctor_identity_package/.mediapipe_models/
+**/infer_single_*/input/
+**/*_run.log
+**/output/result.txt
+
+# --- Python 构建与缓存（子进程内 pip install / 编译产物）---
+**/__pycache__/
+**/*.py[cod]
+**/*$py.class
+**/.pytest_cache/
+**/.mypy_cache/
+**/.ruff_cache/
+**/.cache/
+**/build/
+**/*.egg-info/
+**/*.so
+**/*.o
+**/dist/

backend/app/algorithm_runner/actionformer_gated/__init__.py

@@ -1,4 +1,4 @@
-"""ActionFormer 实时段落检测 + 41 类耗材：使用 ``refs/5.15`` 源码，输入为 RTSP。"""
+"""ActionFormer 实时段落检测 + 41 类耗材：使用 ``algorithm_subprocesses/5.15`` 源码，输入为 RTSP。"""
 
 from app.algorithm_runner.actionformer_gated.runner import (
     ActionFormerSegmentRecord,

backend/app/algorithm_runner/actionformer_gated/inference.py

@@ -1,6 +1,6 @@
-"""ActionFormer 加载与单视频 forward（使用 refs/5.15 vendor 源码）。
+"""ActionFormer 加载与单视频 forward（使用 algorithm_subprocesses/5.15 vendor 源码）。
 
-复刻 refs/5.15 ``run_haocai_actionformer_consumables_e2e`` 写出的 ``infer_single.yaml``
+复刻 algorithm_subprocesses/5.15 ``run_haocai_actionformer_consumables_e2e`` 写出的 ``infer_single.yaml``
 推理配置（``haocai_main_perspective_videoswin``）：input_dim=768, num_classes=1,
 feat_stride=32, num_frames=64, max_seq_len=2304, fpn_type=identity,
 n_mha_win_size=19, embd_dim=fpn_dim=head_dim=256, n_head=4, use_abs_pe=False。
@@ -35,7 +35,7 @@ def _make_inference_cfg(
     feat_stride: int,
     num_frames: int,
 ) -> dict:
-    """与 refs/5.15 e2e ``write_infer_yaml`` 同语义的推理时 cfg dict。"""
+    """与 algorithm_subprocesses/5.15 e2e ``write_infer_yaml`` 同语义的推理时 cfg dict。"""
 
     cfg = deepcopy(load_default_config())
     cfg["dataset_name"] = "thumos"

backend/app/algorithm_runner/actionformer_gated/runner.py

@@ -1,6 +1,6 @@
 """Online ActionFormer 子进程算法核心。
 
-把 refs/5.15 ``run_haocai_actionformer_consumables_e2e`` 的批处理流水线改造为可吃 RTSP
+把 algorithm_subprocesses/5.15 ``run_haocai_actionformer_consumables_e2e`` 的批处理流水线改造为可吃 RTSP
 的实时近似：
 
 * 逐帧维护 64 帧（``CLIP_LEN * FRAME_STRIDE``）的 RGB 224x224 ring。
@@ -90,7 +90,7 @@ class _FrameClsState:
 
 @dataclass
 class _Feature:
-    """特征缓冲单元：对应 refs/5.15 离线 ``feats[i]``。"""
+    """特征缓冲单元：对应 algorithm_subprocesses/5.15 离线 ``feats[i]``。"""
 
     t_center: float  # 该 clip 中心对应的流时间（秒）
     feat: np.ndarray  # shape [INPUT_DIM] float32
@@ -175,7 +175,7 @@ def _to_segment_records(
 
 
 class OnlineActionFormerRunner:
-    """RTSP 实时版的 refs/5.15 流水线，公共 API 与 ``TearGatedSegmentRunner`` 同形。"""
+    """RTSP 实时版的 algorithm_subprocesses/5.15 流水线，公共 API 与 ``TearGatedSegmentRunner`` 同形。"""
 
     def __init__(
         self,
@@ -404,7 +404,7 @@ class OnlineActionFormerRunner:
         self._frame_ring_t.append(float(t_abs))
 
     def _maybe_extract_videoswin_feature(self) -> None:
-        # 与 refs/5.15 离线对齐：feature i 对应 clip 起始帧 s = i * feat_stride_frames，
+        # 与 algorithm_subprocesses/5.15 离线对齐：feature i 对应 clip 起始帧 s = i * feat_stride_frames，
         # clip 末尾帧 s + (clip_len - 1) * frame_stride。当 ring 已塞满（clip_span
         # 帧），且当前 frame_idx == s + clip_span - 1（即 (frame_idx + 1) %
         # feat_stride_frames == 0 且 frame_idx >= clip_span - 1）即可产新特征。

backend/app/algorithm_runner/actionformer_gated/segment_helpers.py

@@ -56,7 +56,7 @@ def haocai_softmax_probs(
     *,
     device: str | None = None,
 ) -> np.ndarray | None:
-    """耗材分类：返回长度 n_cls 的 softmax 概率向量（与 refs/5.15 同语义）。"""
+    """耗材分类：返回长度 n_cls 的 softmax 概率向量（与 algorithm_subprocesses/5.15 同语义）。"""
     if crop.size == 0:
         return None
     r = cls_model.predict(crop, imgsz=imgsz, verbose=False, device=device)[0]

backend/app/algorithm_runner/actionformer_gated/videoswin.py

@@ -1,4 +1,4 @@
-"""VideoSwin (Swin3D-T, Kinetics-400) feature extractor backed by refs/5.15.
+"""VideoSwin (Swin3D-T, Kinetics-400) feature extractor backed by algorithm_subprocesses/5.15.
 
 The online runner keeps a small wrapper API, but model construction and clip
 preprocessing are delegated to the configured reference bundle's
@@ -50,7 +50,7 @@ class VideoSwinFeatureExtractor:
     """Thin wrapper over swin3d_t + preprocess_clip.
 
     输入：``frames_rgb`` 为 32 帧（``CLIP_LEN``）RGB 帧（``np.ndarray[H, W, 3]``）。
-    输出：``[INPUT_DIM]`` 一维特征张量（CPU），与 refs/5.15 离线提特征同语义。
+    输出：``[INPUT_DIM]`` 一维特征张量（CPU），与 algorithm_subprocesses/5.15 离线提特征同语义。
     """
 
     def __init__(self, *, device: torch.device, image_size: int = 224) -> None:

backend/app/algorithm_runner/cli.py

@@ -1,6 +1,6 @@
 """子进程入口：读 RTSP + 白名单 JSON，段级结果追加写入 JSONL。
 
-基于 refs/5.15 ``actionformer_gated`` 流水线（VideoSwin → ActionFormer + 手检/好坏帧/
+基于 algorithm_subprocesses/5.15 ``actionformer_gated`` 流水线（VideoSwin → ActionFormer + 手检/好坏帧/
 耗材分类投票）；父进程通过 ``--events-jsonl`` tail JSONL 即可消费段事件。
 """
 

backend/app/algorithm_runner/reference_bundle_runtime.py

@@ -11,7 +11,7 @@ from typing import Any
 import yaml
 
 REPO_ROOT = Path(__file__).resolve().parents[2]
-DEFAULT_REFERENCE_BUNDLE_RELATIVE = "refs/5.15"
+DEFAULT_REFERENCE_BUNDLE_RELATIVE = "algorithm_subprocesses/5.15"
 
 
 def configured_reference_bundle_relative() -> str:

backend/app/baked/algorithm.py

@@ -60,7 +60,7 @@ HAND_DETECTION_MIN_CROP_PX: int = 64
 HAND_DETECTION_DEVICE: str = ""
 
 
-# --- refs/5.15 ActionFormer 实时版（VideoSwin → ActionFormer + 手检/好坏帧/耗材投票）---
+# --- algorithm_subprocesses/5.15 ActionFormer 实时版（VideoSwin → ActionFormer + 手检/好坏帧/耗材投票）---
 # 默认开启并与常见 demo 主摄 id 对齐；若缺少配置引用包 weights 下权重，开录时该路会记录异常。
 ACTIONFORMER_ENABLED: bool = True
 # 检查期：单路算法固定拉流 or-cam-03（恢复按请求首路时请改回 "" 或按需配置）。
@@ -69,7 +69,7 @@ ACTIONFORMER_PRIMARY_CAMERA_ID: str = "or-cam-03"
 # RTSP 帧率假设（用于 frame_idx → t_abs 推算与 ActionFormer fps 入参；线上需与摄像机一致）。
 ACTIONFORMER_ASSUMED_FPS: float = 25.0
 
-# 与 refs/5.15 ``run_haocai_actionformer_consumables_e2e`` 写出的 infer_single.yaml 同语义的 VideoSwin 参数：
+# 与 algorithm_subprocesses/5.15 ``run_haocai_actionformer_consumables_e2e`` 写出的 infer_single.yaml 同语义的 VideoSwin 参数：
 # clip_len 32, frame_stride 2, feat_stride_frames 32 → feat_num_frames=64。
 VIDEOSWIN_CLIP_LEN: int = 32
 VIDEOSWIN_FRAME_STRIDE: int = 2
@@ -85,7 +85,7 @@ ACTIONFORMER_HAOCAI_WEIGHTS: str = default_actionformer_haocai_weights_path()
 ACTIONFORMER_TEAR_WEIGHTS: str = default_actionformer_tear_weights_path()
 ACTIONFORMER_NUM_CLASSES: int = 1  # 单类（Action）划段，与 e2e infer_single.yaml 对齐
 
-# 与 refs/5.15 e2e 默认对齐的运行时阈值
+# 与 algorithm_subprocesses/5.15 e2e 默认对齐的运行时阈值
 ACTIONFORMER_DET_CONF: float = 0.5
 ACTIONFORMER_PAD_RATIO: float = 0.30
 ACTIONFORMER_DET_IMGSZ: int = 640

backend/app/config.py

@@ -193,9 +193,9 @@ class Settings(BaseSettings):
         default="vision",
         validation_alias=AliasChoices("VIDEO_RESULT_DOCTOR_ID", "video_result_doctor_id"),
     )
-    #: 算法引用包目录（相对 backend 根目录，如 ``refs/5.15``）；可用环境变量 ``REFERENCE_BUNDLE_RELATIVE`` 覆盖。
+    #: 算法子进程包目录（相对 backend 根目录，如 ``algorithm_subprocesses/5.15``）；可用环境变量 ``REFERENCE_BUNDLE_RELATIVE`` 覆盖。
     reference_bundle_relative: str = Field(
-        default="refs/5.15",
+        default="algorithm_subprocesses/5.15",
         validation_alias=AliasChoices("REFERENCE_BUNDLE_RELATIVE", "reference_bundle_relative"),
     )
 

backend/app/resources/consumable_classifier_labels.yaml

@@ -1,4 +1,4 @@
-# 与训练 data.yaml 类名一致；label_id 来自 refs/商品信息表.xlsx（商品名称→产品编码；同名多规格以 / 连接）。
+# 与训练 data.yaml 类名一致；label_id 来自 algorithm_subprocesses 离线包内商品信息表.xlsx（商品名称→产品编码；同名多规格以 / 连接）。
 # 推理时以权重内嵌 names 为准；本文件供业务 label_id 与开录空 candidate 全量类名。
 names:
   0: MCuⅡ功能性宫内节育器

backend/app/routers/demo_orch.py

@@ -71,7 +71,7 @@ def _orchestrate_write_rtsp_host() -> str:
     response_model=VideoBatchSurgeryResponse,
     summary="非实时精确模式：上传单路 MP4 并跑配置引用包 batch",
     description=(
-        "仅当 DEMO_ORCHESTRATOR_ENABLED=true。保存上传视频，调用配置引用包 main.py（默认 refs/5.15），"
+        "仅当 DEMO_ORCHESTRATOR_ENABLED=true。保存上传视频，调用配置算法子进程包 main.py（默认 algorithm_subprocesses/5.15），"
         "解析 TSV 后写入最终结果，并调用 visualize_result_video.py 生成带标签视频。"
     ),
 )

backend/app/services/video_batch_runner.py

@@ -38,7 +38,7 @@ from app.domain.consumption import SurgeryConsumptionStored
 
 @dataclass(frozen=True)
 class ReferenceDoctorInfo:
-    """Parsed from refs/5.15 result footer line ``医生信息：...``."""
+    """Parsed from algorithm_subprocesses/5.15 result footer line ``医生信息：...``."""
 
     doctor_id: str
     doctor_name: str | None
@@ -362,7 +362,7 @@ _DOCTOR_ID_ONLY_RE = re.compile(
 
 
 def parse_reference_doctor_info(path: Path) -> ReferenceDoctorInfo | None:
-    """Read ``医生信息：姓名 (id=...)`` footer appended by refs/5.15 orchestrator."""
+    """Read ``医生信息：姓名 (id=...)`` footer appended by algorithm_subprocesses/5.15 orchestrator."""
 
     if not path.is_file():
         return None
@@ -421,7 +421,7 @@ def parse_reference_doctor_info(path: Path) -> ReferenceDoctorInfo | None:
 
 
 def is_reference_result_complete(path: Path) -> bool:
-    """True when refs/5.15 orchestrator has finished writing the TSV (incl. footer)."""
+    """True when algorithm_subprocesses/5.15 orchestrator has finished writing the TSV (incl. footer)."""
 
     if not path.is_file() or path.stat().st_size <= 0:
         return False
@@ -911,7 +911,7 @@ class VideoBatchRunner:
                 config_path=run_files.config_path.resolve(),
             )
             logger.info(
-                "reference batch starting for surgery_id={} (refs main.py, work_dir={})",
+                "reference batch starting for surgery_id={} (algorithm_subprocesses main.py, work_dir={})",
                 surgery_id,
                 cache_work_dir,
             )

backend/pyproject.toml

@@ -34,9 +34,9 @@ dependencies = [
     "scipy>=1.14.0",
     "pandas>=2.2.0",
     "openpyxl>=3.1.0",
-    # refs/5-6-code ActionFormer eval.py 导入 libs.datasets → anet.py 需要 h5py
+    # algorithm_subprocesses/5-6-code ActionFormer eval.py 导入 libs.datasets → anet.py 需要 h5py
     "h5py>=3.13.0",
-    # refs actionformer libs/utils/metrics.py（随 datasets 链路导入）
+    # algorithm_subprocesses actionformer libs/utils/metrics.py（随 datasets 链路导入）
     "joblib>=1.4.2",
     "mediapipe>=0.10.35",
 ]

backend/tests/test_video_batch_runner.py

@@ -272,13 +272,13 @@ def test_build_reference_visualization_command_uses_hand_model_and_result_tsv(
 
 def test_build_reference_command_uses_5_15_main_py(tmp_path: Path) -> None:
     cmd = build_reference_command(
-        bundle_dir=tmp_path / "refs" / "5.15",
+        bundle_dir=tmp_path / "algorithm_subprocesses" / "5.15",
         config_path=tmp_path / "config.yaml",
     )
 
     assert cmd[:3] == ["uv", "run", "python"]
     assert cmd[3:5] == ["-X", "faulthandler"]
-    assert cmd[5].endswith("refs/5.15/main.py")
+    assert cmd[5].endswith("algorithm_subprocesses/5.15/main.py")
     assert cmd[6:] == ["--config", str(tmp_path / "config.yaml")]
 
 
@@ -286,7 +286,7 @@ def test_video_batch_runner_uses_reference_bundle_relative_env_override(
     tmp_path: Path,
     monkeypatch,
 ) -> None:
-    bundle = tmp_path / "refs" / "custom"
+    bundle = tmp_path / "algorithm_subprocesses" / "custom"
     _write_minimal_reference_bundle(bundle)
     video = tmp_path / "case.mp4"
     video.write_bytes(b"same-video")
@@ -305,7 +305,7 @@ def test_video_batch_runner_uses_reference_bundle_relative_env_override(
         output.write_text(_complete_result_tsv_body(), encoding="utf-8")
         return _Proc()
 
-    monkeypatch.setenv("REFERENCE_BUNDLE_RELATIVE", "refs/custom")
+    monkeypatch.setenv("REFERENCE_BUNDLE_RELATIVE", "algorithm_subprocesses/custom")
     monkeypatch.setattr(reference_bundle_runtime, "REPO_ROOT", tmp_path)
     monkeypatch.setattr("app.services.video_batch_runner.subprocess.run", fake_run)
 

clients/demo-client/index.html

@@ -324,7 +324,7 @@
         <p class="small muted" style="margin:8px 0 0">
           <label style="display:inline-flex;align-items:flex-start;gap:8px;cursor:pointer;max-width:52rem">
             <input type="checkbox" id="video-batch-mode" style="margin-top:2px" />
-            <span><strong>非实时精确模式</strong>：使用调试区「路 1」上传的完整 MP4，调用主 FastAPI 的 <code>POST /internal/demo/video-batch-surgery</code>，复用 <code>refs/5.15/main.py</code> 并在完成后生成带标签视频。该模式不会启动 RTSP 实时会话，处理完成后用查询结果接口查看。</span>
+            <span><strong>非实时精确模式</strong>：使用调试区「路 1」上传的完整 MP4，调用主 FastAPI 的 <code>POST /internal/demo/video-batch-surgery</code>，复用 <code>algorithm_subprocesses/5.15/main.py</code> 并在完成后生成带标签视频。该模式不会启动 RTSP 实时会话，处理完成后用查询结果接口查看。</span>
           </label>
         </p>
         <div class="actions">
@@ -549,7 +549,7 @@
     player.onloadeddata = () => {
       if (hint) {
         hint.innerHTML =
-          `已加载手部打框+耗材标签视频（refs/5.15 visualize_result_video.py）：` +
+          `已加载手部打框+耗材标签视频（algorithm_subprocesses/5.15 visualize_result_video.py）：` +
           `<a href="${src}" target="_blank" rel="noreferrer">${path}</a>`;
       }
     };

clients/demo-client/labels.yaml

@@ -1,4 +1,4 @@
-# 与训练 data.yaml 类名一致；label_id 来自 refs/商品信息表.xlsx（商品名称→产品编码；同名多规格以 / 连接）。
+# 与训练 data.yaml 类名一致；label_id 来自 algorithm_subprocesses 离线包内商品信息表.xlsx（商品名称→产品编码；同名多规格以 / 连接）。
 # 推理时以权重内嵌 names 为准；本文件供业务 label_id 与开录空 candidate 全量类名。
 names:
   0: MCuⅡ功能性宫内节育器