Fix SAM CUDA hard-code (#18153)

Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

ultralytics/models/sam/modules/sam.py

@@ -685,11 +685,11 @@ class SAM2Model(torch.nn.Module):
                 if prev is None:
                     continue  # skip padding frames
                 # "maskmem_features" might have been offloaded to CPU in demo use cases,
-                # so we load it back to GPU (it's a no-op if it's already on GPU).
-                feats = prev["maskmem_features"].cuda(non_blocking=True)
+                # so we load it back to inference device (it's a no-op if it's already on device).
+                feats = prev["maskmem_features"].to(device=device, non_blocking=True)
                 to_cat_memory.append(feats.flatten(2).permute(2, 0, 1))
                 # Spatial positional encoding (it might have been offloaded to CPU in eval)
-                maskmem_enc = prev["maskmem_pos_enc"][-1].cuda()
+                maskmem_enc = prev["maskmem_pos_enc"][-1].to(device=device)
                 maskmem_enc = maskmem_enc.flatten(2).permute(2, 0, 1)
                 # Temporal positional encoding
                 maskmem_enc = maskmem_enc + self.maskmem_tpos_enc[self.num_maskmem - t_pos - 1]