Add deep training acceptance checks

backend/app/acceptance.py

@@ -62,6 +62,66 @@ MMSEG_FULL_BUILD_SNIPPET = (
 )
 
 
+SEGMODEL_TRAIN_STEP_SNIPPET = (
+    "import torch, segmentation_models_pytorch as smp; "
+    "torch.manual_seed(7); "
+    "model=smp.Unet(encoder_name='resnet18', encoder_weights=None, classes=2).train(); "
+    "inputs=torch.randn(2,3,64,64); "
+    "targets=torch.randint(0,2,(2,64,64)); "
+    "optimizer=torch.optim.SGD(model.parameters(), lr=1e-3); "
+    "outputs=model(inputs); "
+    "loss=torch.nn.functional.cross_entropy(outputs, targets); "
+    "loss.backward(); optimizer.step(); "
+    "print('loss', round(float(loss.detach()), 6), 'shape', tuple(outputs.shape))"
+)
+
+
+def _yolo_tiny_train_snippet(root: Path, weight: Path) -> str:
+    return (
+        "import shutil, cv2, numpy as np; "
+        "from pathlib import Path; "
+        "from ultralytics import YOLO; "
+        f"root=Path({str(root)!r}); weight={str(weight)!r}; "
+        "shutil.rmtree(root, ignore_errors=True); "
+        "[ (root / item).mkdir(parents=True, exist_ok=True) for item in ['images/train','images/val','labels/train','labels/val','runs'] ]; "
+        "image=np.zeros((64,64,3), dtype=np.uint8); "
+        "cv2.rectangle(image, (16,16), (48,48), (255,255,255), -1); "
+        "label='0 0.25 0.25 0.75 0.25 0.75 0.75 0.25 0.75\\n'; "
+        "\nfor split in ['train','val']:\n"
+        "    cv2.imwrite(str(root / 'images' / split / 'sample.jpg'), image)\n"
+        "    (root / 'labels' / split / 'sample.txt').write_text(label, encoding='utf-8')\n"
+        "(root / 'data.yaml').write_text('path: '+str(root)+'\\ntrain: images/train\\nval: images/val\\nnc: 1\\nnames:\\n  0: object\\n', encoding='utf-8'); "
+        "model=YOLO(weight); "
+        "model.train(data=str(root/'data.yaml'), epochs=1, imgsz=64, batch=1, workers=0, device='cpu', project=str(root/'runs'), name='tiny', exist_ok=True, verbose=False, plots=False, val=False); "
+        "results=root/'runs'/'tiny'/'results.csv'; best=root/'runs'/'tiny'/'weights'/'best.pt'; "
+        "assert results.exists() and results.stat().st_size > 0; "
+        "assert best.exists() and best.stat().st_size > 0; "
+        "print('results', results, results.stat().st_size, 'best', best.stat().st_size)"
+    )
+
+
+def _mmseg_train_step_snippet(config_path: Path) -> str:
+    return (
+        "import torch; "
+        "from mmengine.config import Config; "
+        "from mmengine.structures import PixelData; "
+        "from mmseg.registry import MODELS; "
+        "from mmseg.structures import SegDataSample; "
+        "from mmseg.utils import register_all_modules; "
+        "register_all_modules(init_default_scope=True); "
+        f"cfg=Config.fromfile({str(config_path)!r}); "
+        "cfg.model.backbone.init_cfg=None; cfg.model.pretrained=None; "
+        "model=MODELS.build(cfg.model).train(); "
+        "sample=SegDataSample(); "
+        "sample.gt_sem_seg=PixelData(data=torch.randint(0,19,(1,64,64), dtype=torch.long)); "
+        "losses=model(torch.randn(1,3,64,64), [sample], mode='loss'); "
+        "loss=sum(value if torch.is_tensor(value) else sum(value) for value in losses.values()); "
+        "optimizer=torch.optim.SGD(model.parameters(), lr=1e-4); "
+        "loss.backward(); optimizer.step(); "
+        "print('loss', round(float(loss.detach()), 6), sorted(losses.keys()))"
+    )
+
+
 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"}
@@ -252,6 +312,56 @@ def latest_acceptance_report() -> dict[str, Any]:
     return json.loads(path.read_text(encoding="utf-8"))
 
 
+def latest_deep_acceptance_report() -> dict[str, Any]:
+    path = settings.project_root / "var" / "acceptance" / "deep_latest.json"
+    if not path.exists():
+        return {"available": False, "path": str(path)}
+    return json.loads(path.read_text(encoding="utf-8"))
+
+
+def run_deep_acceptance() -> dict[str, Any]:
+    """Run minimal training loops for each model family without full datasets."""
+    acceptance_root = settings.project_root / "var" / "acceptance"
+    run_id = uuid.uuid4().hex[:8]
+    fixture_root = acceptance_root / f"deep_{run_id}"
+    fixture_root.mkdir(parents=True, exist_ok=True)
+
+    yolo_weight = settings.source_root / "Seg_All_In_One_YoloModel" / "yolo11n-seg.pt"
+    mmseg_config = settings.source_root / "Seg_All_In_One_MMSeg" / "configs" / "fcn" / "fcn_r18-d8_4xb2-80k_cityscapes-512x1024.py"
+
+    checks = [
+        {
+            "name": "segmodel_tiny_train_step",
+            "passed": False,
+            "detail": _run_snippet(SEGMODEL_TRAIN_STEP_SNIPPET, timeout=90),
+        },
+        {
+            "name": "yolo_tiny_segment_train_epoch",
+            "passed": False,
+            "detail": _run_snippet(_yolo_tiny_train_snippet(fixture_root / "yolo_tiny", yolo_weight), timeout=180),
+        },
+        {
+            "name": "mmseg_tiny_train_step",
+            "passed": False,
+            "detail": _run_conda_snippet(settings.mmseg_conda_env, _mmseg_train_step_snippet(mmseg_config), timeout=120),
+        },
+    ]
+    for check in checks:
+        check["passed"] = bool(check["detail"].get("passed"))
+
+    report = {
+        "available": True,
+        "run_id": run_id,
+        "fixture_root": str(fixture_root),
+        "passed": all(item["passed"] for item in checks),
+        "checks": checks,
+        "created_at": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
+    }
+    latest = acceptance_root / "deep_latest.json"
+    latest.write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8")
+    return report
+
+
 def run_live_acceptance(base_url: str = "http://127.0.0.1:8010") -> dict[str, Any]:
     """Run a lightweight end-to-end smoke against the live API and job runner."""
     acceptance_root = settings.project_root / "var" / "acceptance"