Add model family readiness smoke

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"