diff --git a/tests/test_visualization/test_local_visualizer.py b/tests/test_visualization/test_local_visualizer.py
index 4be96d3ba..7b94ec223 100644
--- a/tests/test_visualization/test_local_visualizer.py
+++ b/tests/test_visualization/test_local_visualizer.py
@@ -7,7 +7,6 @@ from unittest import TestCase
 import cv2
 import mmcv
 import numpy as np
-import pytest
 import torch
 from mmengine.data import PixelData
 
@@ -29,14 +28,11 @@ class TestSegLocalVisualizer(TestCase):
         gt_sem_seg_data = dict(data=torch.randint(0, num_class, (1, h, w)))
         gt_sem_seg = PixelData(**gt_sem_seg_data)
 
-        @pytest.mark.skipif(
-            not torch.cuda.is_available(), reason='CUDA not available')
-        @pytest.mark.parametrize('gt_sem_seg', (gt_sem_seg, gt_sem_seg.cuda()))
         def test_add_datasample_forward(gt_sem_seg):
             gt_seg_data_sample = SegDataSample()
             gt_seg_data_sample.gt_sem_seg = gt_sem_seg
 
-            with tempfile.TemporaryDirectory(dir='temp_dir') as tmp_dir:
+            with tempfile.TemporaryDirectory() as tmp_dir:
                 seg_local_visualizer = SegLocalVisualizer(
                     vis_backends=[dict(type='LocalVisBackend')],
                     save_dir=tmp_dir)
@@ -81,6 +77,10 @@ class TestSegLocalVisualizer(TestCase):
                     osp.join(tmp_dir, 'vis_data', 'vis_image',
                              out_file + '_0.png'), (h, w, 3))
 
+        if torch.cuda.is_available():
+            test_add_datasample_forward(gt_sem_seg.cuda())
+        test_add_datasample_forward(gt_sem_seg)
+
     def test_cityscapes_add_datasample(self):
         h = 128
         w = 256
@@ -103,16 +103,14 @@ class TestSegLocalVisualizer(TestCase):
         gt_sem_seg_data = dict(data=sem_seg)
         gt_sem_seg = PixelData(**gt_sem_seg_data)
 
-        @pytest.mark.skipif(
-            not torch.cuda.is_available(), reason='CUDA not available')
-        @pytest.mark.parametrize('gt_sem_seg', (gt_sem_seg, gt_sem_seg.cuda()))
         def test_cityscapes_add_datasample_forward(gt_sem_seg):
             gt_seg_data_sample = SegDataSample()
             gt_seg_data_sample.gt_sem_seg = gt_sem_seg
-            with tempfile.TemporaryDirectory(dir='temp_dir') as tmp_dir:
+
+            with tempfile.TemporaryDirectory() as tmp_dir:
                 seg_local_visualizer = SegLocalVisualizer(
                     vis_backends=[dict(type='LocalVisBackend')],
-                    save_dir='temp_dir')
+                    save_dir=tmp_dir)
                 seg_local_visualizer.dataset_meta = dict(
                     classes=('road', 'sidewalk', 'building', 'wall', 'fence',
                              'pole', 'traffic light', 'traffic sign',
@@ -165,6 +163,10 @@ class TestSegLocalVisualizer(TestCase):
                     osp.join(tmp_dir, 'vis_data', 'vis_image',
                              out_file + '_0.png'), (h, w, 3))
 
+        if torch.cuda.is_available():
+            test_cityscapes_add_datasample_forward(gt_sem_seg.cuda())
+        test_cityscapes_add_datasample_forward(gt_sem_seg)
+
     def _assert_image_and_shape(self, out_file, out_shape):
         assert os.path.exists(out_file)
         drawn_img = cv2.imread(out_file)