remove torch.cuda.is_available

app.py

@@ -226,14 +226,13 @@ def preprocess_video_in(
     input_labels = []
 
     predictor = build_sam2_video_predictor(model_cfg, sam2_checkpoint, device="cpu")
-    if torch.cuda.is_available():
-        predictor.to("cuda")
-        if inference_state:
-            inference_state["device"] = "cuda"
-        if torch.cuda.get_device_properties(0).major >= 8:
-            torch.backends.cuda.matmul.allow_tf32 = True
-            torch.backends.cudnn.allow_tf32 = True
-        torch.autocast(device_type="cuda", dtype=torch.bfloat16).__enter__()
+    predictor.to("cuda")
+    if inference_state:
+        inference_state["device"] = "cuda"
+    if torch.cuda.get_device_properties(0).major >= 8:
+        torch.backends.cuda.matmul.allow_tf32 = True
+        torch.backends.cudnn.allow_tf32 = True
+    torch.autocast(device_type="cuda", dtype=torch.bfloat16).__enter__()
     inference_state = predictor.init_state(video_path=video_path)
 
     return [
@@ -259,14 +258,13 @@ def segment_with_points(
     evt: gr.SelectData,
 ):
     predictor = build_sam2_video_predictor(model_cfg, sam2_checkpoint, device="cpu")
-    if torch.cuda.is_available():
-        predictor.to("cuda")
-        if inference_state:
-            inference_state["device"] = "cuda"
-        if torch.cuda.get_device_properties(0).major >= 8:
-            torch.backends.cuda.matmul.allow_tf32 = True
-            torch.backends.cudnn.allow_tf32 = True
-        torch.autocast(device_type="cuda", dtype=torch.bfloat16).__enter__()
+    predictor.to("cuda")
+    if inference_state:
+        inference_state["device"] = "cuda"
+    if torch.cuda.get_device_properties(0).major >= 8:
+        torch.backends.cuda.matmul.allow_tf32 = True
+        torch.backends.cudnn.allow_tf32 = True
+    torch.autocast(device_type="cuda", dtype=torch.bfloat16).__enter__()
 
     input_points.append(evt.index)
     print(f"TRACKING INPUT POINT: {input_points}")
@@ -351,14 +349,13 @@ def propagate_to_all(
     inference_state,
 ):
     predictor = build_sam2_video_predictor(model_cfg, sam2_checkpoint, device="cpu")
-    if torch.cuda.is_available():
-        predictor.to("cuda")
-        if inference_state:
-            inference_state["device"] = "cuda"
-        if torch.cuda.get_device_properties(0).major >= 8:
-            torch.backends.cuda.matmul.allow_tf32 = True
-            torch.backends.cudnn.allow_tf32 = True
-        torch.autocast(device_type="cuda", dtype=torch.bfloat16).__enter__()
+    predictor.to("cuda")
+    if inference_state:
+        inference_state["device"] = "cuda"
+    if torch.cuda.get_device_properties(0).major >= 8:
+        torch.backends.cuda.matmul.allow_tf32 = True
+        torch.backends.cudnn.allow_tf32 = True
+    torch.autocast(device_type="cuda", dtype=torch.bfloat16).__enter__()
 
     if len(input_points) == 0 or video_in is None or inference_state is None:
         return None