diff --git a/test.py b/test.py
index e6c66ed8..6ac47f4d 100644
--- a/test.py
+++ b/test.py
@@ -108,10 +108,10 @@ def test(
                         correct.append(0)
 
             # Compute Average Precision (AP) per class
-            AP, AP_class, R, P = ap_per_class(tp=correct,
-                                              conf=detections[:, 4],
-                                              pred_cls=detections[:, 6],
-                                              target_cls=target_cls)
+            AP, AP_class, R, P = ap_per_class(tp=np.array(correct),
+                                              conf=detections[:, 4].cpu().numpy(),
+                                              pred_cls=detections[:, 6].cpu().numpy(),
+                                              target_cls=target_cls.cpu().numpy())
 
             # Accumulate AP per class
             AP_accum_count += np.bincount(AP_class, minlength=nC)
diff --git a/utils/utils.py b/utils/utils.py
index c1a1969a..37bfd8e9 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -92,7 +92,7 @@ def weights_init_normal(m):
 
 def xyxy2xywh(x):
     # Convert bounding box format from [x1, y1, x2, y2] to [x, y, w, h]
-    y = torch.zeros(x.shape) if x.dtype is torch.float32 else np.zeros(x.shape)
+    y = torch.zeros_like(x) if x.dtype is torch.float32 else np.zeros_like(x)
     y[:, 0] = (x[:, 0] + x[:, 2]) / 2
     y[:, 1] = (x[:, 1] + x[:, 3]) / 2
     y[:, 2] = x[:, 2] - x[:, 0]
@@ -102,7 +102,7 @@ def xyxy2xywh(x):
 
 def xywh2xyxy(x):
     # Convert bounding box format from [x, y, w, h] to [x1, y1, x2, y2]
-    y = torch.zeros(x.shape) if x.dtype is torch.float32 else np.zeros(x.shape)
+    y = torch.zeros_like(x) if x.dtype is torch.float32 else np.zeros_like(x)
     y[:, 0] = (x[:, 0] - x[:, 2] / 2)
     y[:, 1] = (x[:, 1] - x[:, 3] / 2)
     y[:, 2] = (x[:, 0] + x[:, 2] / 2)
@@ -134,9 +134,6 @@ def ap_per_class(tp, conf, pred_cls, target_cls):
         The average precision as computed in py-faster-rcnn.
     """
 
-    # lists/pytorch to numpy
-    tp, conf, pred_cls, target_cls = np.array(tp), np.array(conf), np.array(pred_cls), np.array(target_cls)
-
     # Sort by objectness
     i = np.argsort(-conf)
     tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]