fix & add trt support

modules/processors/frame/face_enhancer.py

@@ -48,6 +48,17 @@ def pre_start() -> bool:
     return True
 
 
+TENSORRT_AVAILABLE = False
+try:
+    import torch_tensorrt
+    TENSORRT_AVAILABLE = True
+except ImportError as im:
+    print(f"TensorRT is not available: {im}")
+    pass
+except Exception as e:
+    print(f"TensorRT is not available: {e}")
+    pass
+
 def get_face_enhancer() -> Any:
     global FACE_ENHANCER
 
@@ -55,16 +66,26 @@ def get_face_enhancer() -> Any:
         if FACE_ENHANCER is None:
             model_path = os.path.join(models_dir, "GFPGANv1.4.pth")
             
-            match platform.system():
+            selected_device = None
-                case "Darwin":  # Mac OS
+            device_priority = []
-                    if torch.backends.mps.is_available():
-                        mps_device = torch.device("mps")
-                        FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1, device=mps_device)  # type: ignore[attr-defined]
-                    else:
-                        FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1)  # type: ignore[attr-defined]
-                case _:  # Other OS
-                    FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1)  # type: ignore[attr-defined]
 
+            if TENSORRT_AVAILABLE and torch.cuda.is_available():
+                selected_device = torch.device("cuda")
+                device_priority.append("TensorRT+CUDA")
+            elif torch.cuda.is_available():
+                selected_device = torch.device("cuda")
+                device_priority.append("CUDA")
+            elif torch.backends.mps.is_available() and platform.system() == "Darwin":
+                selected_device = torch.device("mps")
+                device_priority.append("MPS")
+            elif not torch.cuda.is_available():
+                selected_device = torch.device("cpu")
+                device_priority.append("CPU")
+            
+            FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1, device=selected_device)
+
+            # for debug:
+            print(f"Selected device: {selected_device} and device priority: {device_priority}")
     return FACE_ENHANCER