Update wall_pipeline.py

wall_pipeline.py

@@ -42,38 +42,71 @@ warnings.filterwarnings("ignore", category=UserWarning)
 # ── PyTorch / CUDA ────────────────────────────────────────────────────────────
 try:
     import torch
-    _TORCH      = True
-    _TORCH_CUDA = torch.cuda.is_available()
-    _DEVICE     = torch.device("cuda" if _TORCH_CUDA else "cpu")
-    if _TORCH_CUDA:
-        print(f"[GPU] PyTorch CUDA  OK  device={torch.cuda.get_device_name(0)}")
-    else:
-        print("[GPU] PyTorch: CUDA not found — CPU tensors")
+    _TORCH = True
+    try:
+        # cuda.is_available() can itself raise if the driver is too old
+        _TORCH_CUDA = torch.cuda.is_available()
+        if _TORCH_CUDA:
+            # Force an actual CUDA context to catch driver-version mismatches
+            torch.zeros(1, device="cuda")
+            print(f"[GPU] PyTorch CUDA  OK  device={torch.cuda.get_device_name(0)}")
+        else:
+            print("[GPU] PyTorch: CUDA not available — CPU tensors")
+    except Exception as _te:
+        _TORCH_CUDA = False
+        print(f"[GPU] PyTorch CUDA  DISABLED  ({type(_te).__name__}: {_te})")
+    _DEVICE = torch.device("cuda" if _TORCH_CUDA else "cpu")
 except ImportError:
     _TORCH = _TORCH_CUDA = False
     _DEVICE = None
-    print("[GPU] PyTorch not installed")
+    print("[GPU] PyTorch not installed — CPU fallback")
 
 # ── CuPy ─────────────────────────────────────────────────────────────────────
+# Import alone is not enough — cupy is lazily linked to libcuda.so and will
+# only raise CUDARuntimeError when the *first allocation* is attempted.
+# We probe with a tiny 1-element array and catch every possible CUDA error.
 try:
-    import cupy as cp
+    import cupy as _cp_test
+    import cupyx.scipy.ndimage as _cpnd_test
+    # Probe: force an actual CUDA context + allocation
+    _probe = _cp_test.zeros(1, dtype=_cp_test.uint8)
+    del _probe
+    # If we get here the driver is compatible
+    import cupy as cp                        # re-bind to public name
     import cupyx.scipy.ndimage as cpnd
     _CUPY = True
     print(f"[GPU] CuPy           OK  version={cp.__version__}")
 except ImportError:
-    cp   = np          # type: ignore[assignment]
-    cpnd = None        # type: ignore[assignment]
+    cp   = np           # type: ignore[assignment]
+    cpnd = None         # type: ignore[assignment]
     _CUPY = False
     print("[GPU] CuPy not installed — NumPy fallback")
+except Exception as _ce:
+    # Catches CUDARuntimeError (driver too old), CUDADriverError, etc.
+    cp   = np           # type: ignore[assignment]
+    cpnd = None         # type: ignore[assignment]
+    _CUPY = False
+    print(f"[GPU] CuPy DISABLED  ({type(_ce).__name__}: {_ce})")
+    print("[GPU] CuPy → NumPy fallback (all operations remain correct)")
 
 # ── OpenCV CUDA ───────────────────────────────────────────────────────────────
 _CV_CUDA = False
 try:
-    _CV_CUDA = cv2.cuda.getCudaEnabledDeviceCount() > 0
-    print(f"[GPU] OpenCV CUDA   {'OK' if _CV_CUDA else 'NO'}"
-          f"  devices={cv2.cuda.getCudaEnabledDeviceCount()}")
+    n_dev = cv2.cuda.getCudaEnabledDeviceCount()
+    if n_dev > 0:
+        # Probe with a tiny upload to catch driver mismatches
+        _probe_mat = cv2.cuda_GpuMat()
+        _probe_mat.upload(np.zeros((2, 2), np.uint8))
+        del _probe_mat
+        _CV_CUDA = True
+        print(f"[GPU] OpenCV CUDA   OK  devices={n_dev}")
+    else:
+        print("[GPU] OpenCV CUDA   NO  (no CUDA-enabled devices)")
 except AttributeError:
-    print("[GPU] OpenCV CUDA module absent")
+    print("[GPU] OpenCV CUDA module absent — CPU morphology")
+except Exception as _oce:
+    _CV_CUDA = False
+    print(f"[GPU] OpenCV CUDA   DISABLED  ({type(_oce).__name__}: {_oce})")
 
 # ── scikit-image skeleton ─────────────────────────────────────────────────────
 try: