diff --git a/object_detection/core/external/setup.py b/object_detection/core/external/setup.py
index cf55a25..f4af595 100644
--- a/object_detection/core/external/setup.py
+++ b/object_detection/core/external/setup.py
@@ -8,12 +8,12 @@ extensions = [
     Extension(
         "bbox",
         ["bbox.pyx"],
-        extra_compile_args=["-Wno-cpp", "-Wno-unused-function"]
+        extra_compile_args=[]
     ),
     Extension(
         "nms",
         ["nms.pyx"],
-        extra_compile_args=["-Wno-cpp", "-Wno-unused-function"]
+        extra_compile_args=[]
     )
 ]
 
diff --git a/object_detection/core/nnet/py_factory.py b/object_detection/core/nnet/py_factory.py
index ead8da0..70c6e03 100644
--- a/object_detection/core/nnet/py_factory.py
+++ b/object_detection/core/nnet/py_factory.py
@@ -119,7 +119,7 @@ class NetworkFactory(object):
     def load_pretrained_params(self, pretrained_model):
         print("loading from {}".format(pretrained_model))
         with open(pretrained_model, "rb") as f:
-            params = torch.load(f)
+            params = torch.load(f, weights_only=False)
             self.model.load_state_dict(params)
 
     def load_params(self, iteration):
diff --git a/object_detection/core/test/cornernet_saccade.py b/object_detection/core/test/cornernet_saccade.py
index 75adc2d..75b978d 100644
--- a/object_detection/core/test/cornernet_saccade.py
+++ b/object_detection/core/test/cornernet_saccade.py
@@ -233,7 +233,7 @@ def prepare_images(db, image, locs, flipped=True):
     input_size = db.configs["input_size"]
     num_patches = locs.shape[0]
 
-    images = torch.cuda.FloatTensor(num_patches, 3, input_size[0], input_size[1]).fill_(0)
+    images = torch.zeros((num_patches, 3, input_size[0], input_size[1]), dtype=torch.float32, device='cuda')
     offsets = np.zeros((num_patches, 2), dtype=np.float32)
     for ind, (y, x, scale) in enumerate(locs[:, :3]):
         crop_height = int(input_size[0] / scale)
@@ -319,10 +319,9 @@ def cornernet_saccade_inference(db, nnet, image, decode_func=batch_decode):
 
     num_iterations = len(att_thresholds)
 
-    im_mean = torch.cuda.FloatTensor(db.mean).reshape(1, 3, 1, 1)
-    im_std = torch.cuda.FloatTensor(db.std).reshape(1, 3, 1, 1)
+    im_mean = torch.tensor(db.mean, dtype=torch.float32, device='cuda').reshape(1, 3, 1, 1)
+    im_std = torch.tensor(db.std, dtype=torch.float32, device='cuda').reshape(1, 3, 1, 1)
 
-    detections = []
     height, width = image.shape[0:2]
 
     image = image / 255.