update torch conditional import

src/orisys/tracker.py

@@ -25,11 +25,15 @@ class Tracker:
         dis_preset = config_tracker["dis_preset"]
         loop_closure_interval = config_tracker["loop_closure_interval"]
         self.__algorithm = config_tracker["algorithm"]
+        
+        # Conditionally import torch only if RAFT algorithm is used
+        self.torch = None
         if self.__algorithm == "raft":
             import torch
+            self.torch = torch
 
 
-        if self.__algorithm == "raft" and (not self.cuda or not torch.cuda.is_available()):
+        if self.__algorithm == "raft" and (not self.cuda or not self.torch.cuda.is_available()):
             self.__algorithm = "dis"
             if self.verbose:
                 print("No CUDA available, falling back to OPENCV DIS optical flow")
@@ -217,7 +221,7 @@ class Tracker:
             frame = np.ascontiguousarray(frame)
         
         # Upload to device as uint8 first to save bandwidth (4x less data than float32)
-        tensor = torch.from_numpy(frame).permute(2, 0, 1)
+        tensor = self.torch.from_numpy(frame).permute(2, 0, 1)
         
         # Pin memory for faster CPU-GPU transfer (similar to DataLoader pin_memory=True)
         if self.__raft_device == 'cuda':
@@ -258,9 +262,9 @@ class Tracker:
         if self.verbose:
             t0 = time.perf_counter()
 
-        with torch.inference_mode():
+        with self.torch.inference_mode():
             if self.__raft_device == 'cuda':
-                with torch.amp.autocast('cuda'):
+                with self.torch.amp.autocast('cuda'):
                     flow = self.__raft_model(prev_tensor, curr_tensor)
             else:
                 flow = self.__raft_model(prev_tensor, curr_tensor)