Add model family readiness smoke

README.md

@@ -54,7 +54,15 @@ classified as supporting artifacts rather than direct web actions.
 The same panel can run `POST /api/acceptance/smoke`, a lightweight live smoke
 that creates an upload dataset, uploads a label, downloads it through the
 artifact API, runs a mock job, checks SSE log streaming, and executes one
-legacy image/label overlay job on tiny generated PNGs.
+legacy image/label overlay job on tiny generated PNGs. It also runs model
+family readiness checks: a SegModel/SMP forward pass, a YOLO segmentation
+prediction on a tiny image, MMSeg config parsing, and local MMSeg pretrained
+weight discovery.
+
+Current `seg_smp` uses `mmcv-lite` because no `torch 2.6/cu124` full `mmcv`
+wheel is available on this machine and `nvcc` is not installed for source
+builds. The acceptance smoke reports MMSeg full model construction as a
+warning until a full `mmcv` build with `mmcv._ext` is installed.
 
 ## Weight Sync
 

backend/app/acceptance.py

@@ -1,6 +1,8 @@
 from __future__ import annotations
 
 import json
+import subprocess
+import sys
 import time
 import uuid
 import urllib.error
@@ -11,6 +13,22 @@ from typing import Any
 from .config import settings
 
 
+def _run_snippet(code: str, cwd: Path | None = None, timeout: int = 60) -> dict[str, Any]:
+    result = subprocess.run(
+        [sys.executable, "-c", code],
+        cwd=str(cwd or settings.project_root),
+        capture_output=True,
+        text=True,
+        timeout=timeout,
+    )
+    return {
+        "passed": result.returncode == 0,
+        "returncode": result.returncode,
+        "stdout": result.stdout[-4000:],
+        "stderr": result.stderr[-4000:],
+    }
+
+
 def _request_json(method: str, url: str, payload: dict[str, Any] | None = None, timeout: int = 10) -> dict[str, Any]:
     data = None
     headers = {"Accept": "application/json"}
@@ -122,6 +140,68 @@ def _write_acceptance_images(root: Path) -> tuple[Path, Path, Path]:
     return image_path, label_path, result_dir
 
 
+def run_model_family_readiness() -> dict[str, Any]:
+    """Exercise the model-family runtime stack without launching full training."""
+    source = settings.source_root
+    yolo_weight = source / "Seg_All_In_One_YoloModel" / "yolo11n-seg.pt"
+    mmseg_config = source / "Seg_All_In_One_MMSeg" / "configs" / "fcn" / "fcn_r18-d8_4xb2-80k_cityscapes-512x1024.py"
+    mmseg_pretrained = source / "Seg_All_In_One_MMSeg" / "My_Local_Model" / "mmcls" / "resnet18.pth"
+
+    checks = [
+        {
+            "name": "segmodel_smp_forward",
+            "required": True,
+            "detail": _run_snippet(
+                "import torch, segmentation_models_pytorch as smp; "
+                "m=smp.Unet(encoder_name='resnet18', encoder_weights=None, classes=2).eval(); "
+                "torch.set_grad_enabled(False); y=m(torch.randn(1,3,64,64)); "
+                "print(tuple(y.shape))"
+            ),
+        },
+        {
+            "name": "yolo_seg_predict_cpu",
+            "required": True,
+            "detail": _run_snippet(
+                "from ultralytics import YOLO; import numpy as np; "
+                f"model=YOLO({str(yolo_weight)!r}); "
+                "r=model.predict(np.zeros((64,64,3), dtype=np.uint8), imgsz=64, verbose=False, save=False, device='cpu'); "
+                "print(len(r), r[0].orig_shape)"
+            ),
+        },
+        {
+            "name": "mmseg_config_parse",
+            "required": True,
+            "detail": _run_snippet(
+                "from mmengine.config import Config; "
+                f"cfg=Config.fromfile({str(mmseg_config)!r}); "
+                "print(cfg.model.type, cfg.train_dataloader.batch_size)"
+            ),
+        },
+        {
+            "name": "mmseg_local_pretrained_weight",
+            "required": True,
+            "detail": {"passed": mmseg_pretrained.exists(), "path": str(mmseg_pretrained), "size": mmseg_pretrained.stat().st_size if mmseg_pretrained.exists() else 0},
+        },
+        {
+            "name": "mmseg_full_model_build",
+            "required": False,
+            "detail": _run_snippet(
+                "from mmengine.config import Config; from mmseg.registry import MODELS; "
+                f"cfg=Config.fromfile({str(mmseg_config)!r}); "
+                "model=MODELS.build(cfg.model); print(type(model).__name__)",
+                timeout=90,
+            ),
+        },
+    ]
+    for check in checks:
+        check["passed"] = bool(check["detail"].get("passed"))
+    return {
+        "passed": all(item["passed"] for item in checks if item["required"]),
+        "warnings": [item for item in checks if not item["required"] and not item["passed"]],
+        "checks": checks,
+    }
+
+
 def latest_acceptance_report() -> dict[str, Any]:
     path = settings.project_root / "var" / "acceptance" / "latest.json"
     if not path.exists():
@@ -188,12 +268,16 @@ def run_live_acceptance(base_url: str = "http://127.0.0.1:8010") -> dict[str, An
         }
     )
 
+    readiness = run_model_family_readiness()
+    checks.append({"name": "model_family_readiness", "passed": readiness["passed"], "detail": readiness})
+
     report = {
         "available": True,
         "run_id": run_id,
         "base_url": base_url,
         "passed": all(item["passed"] for item in checks),
         "checks": checks,
+        "model_family_readiness": readiness,
         "created_at": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
     }
     latest = acceptance_root / "latest.json"

frontend/src/main.tsx

@@ -78,6 +78,11 @@ type AcceptancePayload = {
   run_id?: string;
   created_at?: string;
   checks?: Array<{ name: string; passed: boolean }>;
+  model_family_readiness?: {
+    passed: boolean;
+    warnings: Array<{ name: string; passed: boolean }>;
+    checks: Array<{ name: string; passed: boolean; required: boolean }>;
+  };
 };
 
 type GpuPayload = {
@@ -539,12 +544,17 @@ function App() {
                 <span>轻量验收</span>
                 <strong>{acceptance?.available === false ? "New" : acceptance?.passed ? "OK" : "Check"}</strong>
               </div>
+              <div>
+                <span>模型族</span>
+                <strong>{acceptance?.model_family_readiness?.passed ? "OK" : "Check"}</strong>
+              </div>
             </div>
             <div className="coverageStatus">
               {(coverage?.unmapped_user_scripts.length ?? 0) === 0 ? (
                 <>
                   <span>当前用户侧脚本已全部映射到网页任务。</span>
                   <span>最近验收：{acceptance?.created_at ?? "尚未运行"} {acceptance?.run_id ? `#${acceptance.run_id}` : ""}</span>
+                  <span>模型族 readiness：{acceptance?.model_family_readiness?.checks?.filter((item) => item.passed).length ?? 0}/{acceptance?.model_family_readiness?.checks?.length ?? 0}，warnings {acceptance?.model_family_readiness?.warnings?.length ?? 0}</span>
                 </>
               ) : (
                 coverage?.unmapped_user_scripts.slice(0, 8).map((item) => <code key={item}>{item}</code>)

frontend/src/styles.css

@@ -399,7 +399,7 @@ textarea {
 
 .coverageGrid {
   display: grid;
-  grid-template-columns: repeat(4, minmax(0, 1fr));
+  grid-template-columns: repeat(5, minmax(0, 1fr));
   gap: 10px;
   margin-bottom: 14px;
 }