Chengfang Lu
e72bc061c5
Major changes: - New frontend (platform/web/): Vite + React 18 + TypeScript + Tailwind - 4-module navigation: 数据送标 / 模型管理 / 车队管理 / 系统管理 - Data catalog with charts (DMS/ADAS/Lane 3-tab view) - Quality review workflow (标注质检): Good/Fine/Bad scoring with auto-advance - Audit enhancements: batch operations, rejection categories, Feishu notifications - Operation audit log (操作日志) - World model simulation studio (仿真工坊) - Dataset version management with snapshots and diff - ADAS 7-class dataset integration (138K images organized + compressed) - User management with Feishu integration and pagination - CRUD/search/filter on all pages, card layout redesign - PIL-optimized image overlay rendering - Auto-snapshot on build, in_review workflow stage - Removed embedded algorithm code (now in workspace)
87 lines
3.0 KiB
Python
87 lines
3.0 KiB
Python
"""CatalogScope 与 registry 对齐。"""
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from __future__ import annotations
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from typing import Any
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import yaml
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from as_platform.config import WORKSPACE
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DOMAIN_LABELS = {"dms": "舱内 DMS", "forward": "前向 ADAS"}
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def format_scope_key(project: str, task: str, mode: str | None = None) -> str:
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if project == "lane":
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return f"lane:{task}"
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if mode:
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return f"dms:{task}:{mode}"
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return f"dms:{task}"
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def _dms_registry_api():
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import sys
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from pathlib import Path
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p = WORKSPACE / "datasets" / "dms" / "scripts"
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if str(p) not in sys.path:
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sys.path.insert(0, str(p))
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from task_registry import get_mode_config, resolve_task_id, train_yaml_key
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return get_mode_config, resolve_task_id, train_yaml_key
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def load_dms_registry() -> dict:
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path = WORKSPACE / "datasets" / "dms" / "datasets.registry.yaml"
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return yaml.safe_load(path.read_text(encoding="utf-8"))
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def load_labeling_registry() -> dict[str, Any]:
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path = WORKSPACE / "datasets" / "labeling.registry.yaml"
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if not path.is_file():
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return {"profiles": {}}
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return yaml.safe_load(path.read_text(encoding="utf-8")) or {"profiles": {}}
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def labeling_profile_key(project: str, task: str, mode: str | None, reg: dict | None = None) -> str:
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if project == "lane":
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return f"lane__{task}"
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get_mode_config, resolve_task_id, train_yaml_key = _dms_registry_api()
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reg = reg or load_dms_registry()
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task, mode = resolve_task_id(task, mode)
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return train_yaml_key(task, mode, reg)
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def enrich_batch_labels(batch: dict[str, Any], reg: dict | None = None) -> dict[str, Any]:
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project = batch.get("project") or "dms"
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task = batch.get("task") or ""
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mode = batch.get("mode")
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out = dict(batch)
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out["scope_key"] = format_scope_key(project, task, mode)
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if project == "dms":
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reg = reg or load_dms_registry()
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get_mode_config, resolve_task_id, _ = _dms_registry_api()
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try:
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task_r, mode_r = resolve_task_id(task, mode)
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mcfg = get_mode_config(task_r, mode_r, reg)
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domain = mcfg.get("domain") or "dms"
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out["domain"] = domain
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out["domain_label"] = DOMAIN_LABELS.get(domain, domain)
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out["task_label"] = mcfg.get("label") or task
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if mode_r:
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modes = (reg.get("tasks") or {}).get(task_r, {}).get("modes") or {}
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out["mode_label"] = (modes.get(mode_r) or {}).get("label") or mode_r
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except Exception:
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out["domain"] = "dms"
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out["domain_label"] = DOMAIN_LABELS["dms"]
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else:
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out["domain_label"] = "车道线 Lane"
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out["task_label"] = task
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try:
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pk = labeling_profile_key(project, task, mode, reg if project == "dms" else None)
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out["labeling_profile"] = pk
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prof = (load_labeling_registry().get("profiles") or {}).get(pk)
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if prof:
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out["export_default"] = prof.get("export_default")
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out["ml_adapter"] = prof.get("ml_adapter")
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except Exception:
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out["labeling_profile"] = None
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return out
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