mv get_gpu_id func to utility

tools/infer/predict_det.py

@@ -101,7 +101,7 @@ class TextDetector(object):
         if args.benchmark:
             import auto_log
             pid = os.getpid()
-            gpu_id = self.get_infer_gpuid()
+            gpu_id = utility.get_infer_gpuid()
             self.autolog = auto_log.AutoLogger(
                 model_name="det",
                 model_precision=args.precision,
@@ -118,19 +118,6 @@ class TextDetector(object):
                 warmup=2,
                 logger=logger)
 
-    def get_infer_gpuid(self):
-        cmd = "nvidia-smi"
-        res = os.popen(cmd).readlines()
-        if len(res) == 0:
-            return None
-        cmd = "env | grep CUDA_VISIBLE_DEVICES"
-        env_cuda = os.popen(cmd).readlines()
-        if len(env_cuda) == 0:
-            return 0
-        else:
-            gpu_id = env_cuda[0].strip().split("=")[1]
-            return int(gpu_id[0])
-
     def order_points_clockwise(self, pts):
         """
         reference from: https://github.com/jrosebr1/imutils/blob/master/imutils/perspective.py

tools/infer/utility.py

@@ -280,6 +280,20 @@ def create_predictor(args, mode, logger):
     return predictor, input_tensor, output_tensors, config
 
 
+def get_infer_gpuid():
+    cmd = "nvidia-smi"
+    res = os.popen(cmd).readlines()
+    if len(res) == 0:
+        return None
+    cmd = "env | grep CUDA_VISIBLE_DEVICES"
+    env_cuda = os.popen(cmd).readlines()
+    if len(env_cuda) == 0:
+        return 0
+    else:
+        gpu_id = env_cuda[0].strip().split("=")[1]
+        return int(gpu_id[0])
+
+
 def draw_e2e_res(dt_boxes, strs, img_path):
     src_im = cv2.imread(img_path)
     for box, str in zip(dt_boxes, strs):