diff --git a/tests/test_visualizer/test_visualizer.py b/tests/test_visualizer/test_visualizer.py
index dc0485fa..fb4b3903 100644
--- a/tests/test_visualizer/test_visualizer.py
+++ b/tests/test_visualizer/test_visualizer.py
@@ -364,21 +364,21 @@ class TestVisualizer(TestCase):
             visualizer.draw_polygons(torch.tensor([[1, 1], [2, 2], [16, 4]]))
 
     def test_draw_binary_masks(self):
-        binary_mask = np.random.randint(0, 2, size=(10, 10)).astype(np.bool)
+        binary_mask = np.random.randint(0, 2, size=(10, 10)).astype(bool)
         visualizer = Visualizer(image=self.image)
         visualizer.draw_binary_masks(binary_mask)
         visualizer.draw_binary_masks(torch.from_numpy(binary_mask))
         # multi binary
-        binary_mask = np.random.randint(0, 2, size=(2, 10, 10)).astype(np.bool)
+        binary_mask = np.random.randint(0, 2, size=(2, 10, 10)).astype(bool)
         visualizer = Visualizer(image=self.image)
         visualizer.draw_binary_masks(binary_mask, colors=['r', (0, 255, 0)])
         # test the error that the size of mask and image are different.
         with pytest.raises(AssertionError):
-            binary_mask = np.random.randint(0, 2, size=(8, 10)).astype(np.bool)
+            binary_mask = np.random.randint(0, 2, size=(8, 10)).astype(bool)
             visualizer.draw_binary_masks(binary_mask)
 
         # test non binary mask error
-        binary_mask = np.random.randint(0, 2, size=(10, 10, 3)).astype(np.bool)
+        binary_mask = np.random.randint(0, 2, size=(10, 10, 3)).astype(bool)
         with pytest.raises(AssertionError):
             visualizer.draw_binary_masks(binary_mask)
 
@@ -495,7 +495,7 @@ class TestVisualizer(TestCase):
 
     def test_chain_call(self):
         visualizer = Visualizer(image=self.image)
-        binary_mask = np.random.randint(0, 2, size=(10, 10)).astype(np.bool)
+        binary_mask = np.random.randint(0, 2, size=(10, 10)).astype(bool)
         visualizer.draw_bboxes(torch.tensor([1, 1, 2, 2])). \
             draw_texts('test', torch.tensor([5, 5])). \
             draw_lines(x_datas=torch.tensor([1, 5]),