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)
268 lines
9.7 KiB
Python
268 lines
9.7 KiB
Python
"""执行动作：优先引擎适配器，fallback as.py CLI。"""
|
||
from __future__ import annotations
|
||
|
||
import json
|
||
import subprocess
|
||
import sys
|
||
from typing import Any
|
||
|
||
from as_platform.config import WORKSPACE, PLATFORM_DIR, LANE_DATA_VIZ_ENABLED
|
||
|
||
if str(WORKSPACE) not in sys.path:
|
||
sys.path.insert(0, str(WORKSPACE))
|
||
if str(PLATFORM_DIR) not in sys.path:
|
||
sys.path.insert(0, str(PLATFORM_DIR))
|
||
|
||
ML_PY = WORKSPACE / "as.py"
|
||
AS_PY = ML_PY
|
||
|
||
LONG_ACTIONS = {"train_dms", "train_lane", "pipeline_dms", "eval_dms", "eval_lane", "visualize_dms", "visualize_lane"}
|
||
|
||
|
||
def _auto_snapshot(project: str, task: str = "") -> None:
|
||
"""build 成功后自动创建数据集版本快照。"""
|
||
try:
|
||
from as_platform.data.versions import create_snapshot
|
||
desc = f"自动快照 · build {project}"
|
||
if task:
|
||
desc += f"/{task}"
|
||
create_snapshot(project, description=desc, author="system")
|
||
except Exception:
|
||
pass # 快照失败不影响 build
|
||
|
||
|
||
def _run_ml(argv: list[str], timeout: int = 7200) -> dict[str, Any]:
|
||
cmd = [sys.executable, str(ML_PY), *argv]
|
||
proc = subprocess.run(cmd, cwd=str(WORKSPACE), capture_output=True, text=True, timeout=timeout)
|
||
if proc.returncode != 0:
|
||
raise RuntimeError(f"as.py 失败 (exit {proc.returncode}):\n{proc.stderr or proc.stdout}")
|
||
return {"ok": True, "stdout": proc.stdout, "stderr": proc.stderr, "command": " ".join(cmd)}
|
||
|
||
|
||
def execute_action(action: str, params: dict[str, Any]) -> dict[str, Any]:
|
||
p = params or {}
|
||
|
||
if action == "train_dms":
|
||
track = p.get("track", "platform")
|
||
if track == "local":
|
||
from algorithms.dms_yolo.adapter import train_local
|
||
return train_local(
|
||
p["task"],
|
||
p.get("mode", "full"),
|
||
p.get("config_overrides"),
|
||
submode=p.get("submode"),
|
||
)
|
||
from algorithms.dms_yolo.adapter import train_platform
|
||
return train_platform(p["task"], p.get("mode", "full"), submode=p.get("submode"))
|
||
|
||
if action == "train_lane":
|
||
track = p.get("track", "platform")
|
||
if track == "local":
|
||
from algorithms.lane_ufld.adapter import train_local
|
||
return train_local(p.get("config_overrides"))
|
||
from algorithms.lane_ufld.adapter import train_platform
|
||
return train_platform()
|
||
|
||
if action == "train_dms_legacy":
|
||
argv = ["train", "dms", p["task"]]
|
||
if p.get("mode"):
|
||
argv.extend(["--mode", str(p["mode"])])
|
||
return _run_ml(argv, timeout=86400)
|
||
|
||
if action == "train_lane_legacy":
|
||
return _run_ml(["train", "lane"], timeout=86400)
|
||
|
||
if action == "build_dms":
|
||
argv = ["build", "dms", p["task"]]
|
||
if p.get("pack"):
|
||
argv.extend(["--pack", str(p["pack"])])
|
||
if p.get("batch"):
|
||
argv.extend(["--batch", str(p["batch"])])
|
||
if p.get("all_sources"):
|
||
argv.append("--all-sources")
|
||
if p.get("dry_run"):
|
||
argv.append("--dry-run")
|
||
if p.get("skip_validate"):
|
||
argv.append("--skip-validate")
|
||
if p.get("no_refresh"):
|
||
argv.append("--no-refresh")
|
||
result = _run_ml(argv)
|
||
# 自动创建数据集快照
|
||
_auto_snapshot("dms", task=p.get("task", ""))
|
||
return result
|
||
|
||
if action == "build_lane":
|
||
result = _run_ml(["build", "lane"])
|
||
_auto_snapshot("lane")
|
||
return result
|
||
|
||
if action == "enable_pack":
|
||
return _run_ml(["enable", p["project"], p["pack"]])
|
||
|
||
if action == "disable_pack":
|
||
return _run_ml(["disable", p["project"], p["pack"]])
|
||
|
||
if action == "eval_dms":
|
||
argv = ["eval", "dms", p["task"]]
|
||
if p.get("save_candidate"):
|
||
argv.append("--save-candidate")
|
||
if p.get("weights"):
|
||
argv.extend(["--weights", str(p["weights"])])
|
||
return _run_ml(argv, timeout=3600)
|
||
|
||
if action == "eval_lane":
|
||
from algorithms.lane_ufld.adapter import eval_task
|
||
|
||
return eval_task(
|
||
model_path=p.get("model_path"),
|
||
data_root=p.get("data_root"),
|
||
test_list=p.get("test_list", "list/test_gt.txt"),
|
||
)
|
||
|
||
if action == "visualize_dms":
|
||
from algorithms.dms_yolo.adapter import visualize_task
|
||
|
||
return visualize_task(
|
||
p["task"],
|
||
weights=p.get("weights"),
|
||
)
|
||
|
||
if action == "visualize_lane":
|
||
if not LANE_DATA_VIZ_ENABLED:
|
||
raise RuntimeError("车道线数据可视化暂未开放")
|
||
from algorithms.lane_ufld.adapter import visualize_task
|
||
|
||
return visualize_task(
|
||
model_path=p.get("model_path"),
|
||
data_root=p.get("data_root"),
|
||
test_list=p.get("test_list", "list/test_gt.txt"),
|
||
)
|
||
|
||
if action == "promote_dms":
|
||
argv = ["promote", "dms", p["task"]]
|
||
if p.get("force"):
|
||
argv.append("--force")
|
||
return _run_ml(argv)
|
||
|
||
if action == "pipeline_dms":
|
||
argv = ["pipeline", "dms", p["task"], "--pack", str(p.get("pack", "dms_v2"))]
|
||
if p.get("batch"):
|
||
argv.extend(["--batch", str(p["batch"])])
|
||
if p.get("all_sources"):
|
||
argv.append("--all-sources")
|
||
if p.get("train"):
|
||
argv.append("--train")
|
||
if p.get("dry_run"):
|
||
argv.append("--dry-run")
|
||
return _run_ml(argv, timeout=86400)
|
||
|
||
if action == "register_batch":
|
||
from as_platform.data.core import register_batch
|
||
|
||
register_batch(
|
||
None, p["project"], p.get("task"), p["batch"],
|
||
pack=p.get("pack"), stage=p.get("stage", "returned"),
|
||
engineer=p.get("engineer"), location=p.get("location", "inbox"),
|
||
)
|
||
return {"ok": True, "stdout": "register_batch ok", "stderr": ""}
|
||
|
||
if action == "delivery_ingest":
|
||
from as_platform.integrations.delivery_ingest import run_delivery_ingest
|
||
|
||
delivery_id = p.get("delivery_id") or ""
|
||
if not delivery_id:
|
||
raise ValueError("缺少 delivery_id")
|
||
result = run_delivery_ingest(delivery_id)
|
||
return {
|
||
"ok": True,
|
||
"stdout": json.dumps(result, ensure_ascii=False),
|
||
"stderr": "",
|
||
"result": result,
|
||
}
|
||
|
||
if action == "analyze_uploaded_dataset":
|
||
from as_platform.data.lake import analyze_uploaded_candidate
|
||
|
||
candidate_id = p["candidate_id"]
|
||
result = analyze_uploaded_candidate(candidate_id)
|
||
return {
|
||
"ok": True,
|
||
"stdout": json.dumps(result, ensure_ascii=False),
|
||
"stderr": "",
|
||
"result": result,
|
||
}
|
||
|
||
if action == "labeling_export":
|
||
from as_platform.db.engine import session_scope
|
||
from as_platform.db.models import LabelingCampaign
|
||
from as_platform.labeling.annotate import resolve_campaign_batch_dir
|
||
from as_platform.labeling.service import get_campaign
|
||
|
||
campaign_id = p.get("campaign_id", "")
|
||
row = get_campaign(campaign_id)
|
||
if not row:
|
||
raise ValueError("campaign not found")
|
||
task = row.get("task") or "dam"
|
||
batch = row.get("batch") or ""
|
||
pack = row.get("pack") or "dms_v2"
|
||
export = row.get("export_default") or "yolo"
|
||
if row.get("project") == "dms" and export in ("yolo", "yolo_pose") and batch:
|
||
scripts_dir = WORKSPACE / "datasets" / "dms" / "scripts"
|
||
if str(scripts_dir) not in sys.path:
|
||
sys.path.insert(0, str(scripts_dir))
|
||
from export_ls_to_yolo import export_batch
|
||
|
||
with session_scope() as db:
|
||
camp = db.get(LabelingCampaign, campaign_id)
|
||
if not camp:
|
||
raise ValueError("campaign not found")
|
||
batch_dir = resolve_campaign_batch_dir(camp)
|
||
export_mode = "pose" if export == "yolo_pose" else "detect"
|
||
conv = export_batch(
|
||
batch_dir,
|
||
task,
|
||
mode=export_mode,
|
||
task_mode=row.get("mode"),
|
||
)
|
||
if conv.get("written", 0) == 0:
|
||
raise ValueError(
|
||
"export_ls_to_yolo: 无有效标注可导出 (written=0); "
|
||
f"skipped_empty={conv.get('skipped_empty')} missing_ann={conv.get('missing_ann')}"
|
||
)
|
||
argv = ["build", "dms", task, "--pack", pack, "--batch", batch]
|
||
result = _run_ml(argv)
|
||
result["export_convert"] = conv
|
||
return result
|
||
if row.get("project") == "lane" and export == "lane_gt_txt":
|
||
scripts_dir = WORKSPACE / "datasets" / "lane" / "scripts"
|
||
if str(scripts_dir) not in sys.path:
|
||
sys.path.insert(0, str(scripts_dir))
|
||
from export_ls_to_lane_gt import export_batch
|
||
|
||
with session_scope() as db:
|
||
camp = db.get(LabelingCampaign, campaign_id)
|
||
if not camp:
|
||
raise ValueError("campaign not found")
|
||
batch_dir = resolve_campaign_batch_dir(camp)
|
||
conv = export_batch(batch_dir)
|
||
if conv.get("written", 0) == 0:
|
||
raise ValueError(
|
||
"export_ls_to_lane_gt: 无有效标注可导出 (written=0); "
|
||
f"skipped_empty={conv.get('skipped_empty')} missing_ann={conv.get('missing_ann')}"
|
||
)
|
||
argv = ["build", "lane"]
|
||
result = _run_ml(argv)
|
||
result["export_convert"] = conv
|
||
return result
|
||
return {
|
||
"ok": True,
|
||
"stdout": json.dumps({"export": export, "campaign": row}, ensure_ascii=False),
|
||
"stderr": "",
|
||
"message": f"export 类型 {export} 暂无 CLI，已记录",
|
||
}
|
||
|
||
if action == "labeling_ml_predict":
|
||
raise ValueError("labeling_ml_predict 已停用")
|
||
|
||
raise ValueError(f"未实现执行: {action}")
|