diff --git a/test/test_functional_tensor.py b/test/test_functional_tensor.py
index e464bf733a8..6c33bd9a548 100644
--- a/test/test_functional_tensor.py
+++ b/test/test_functional_tensor.py
@@ -12,20 +12,25 @@ class Tester(unittest.TestCase):
 
     def test_vflip(self):
         img_tensor = torch.randn(3, 16, 16)
+        img_tensor_clone = img_tensor.clone()
         vflipped_img = F_t.vflip(img_tensor)
         vflipped_img_again = F_t.vflip(vflipped_img)
         self.assertEqual(vflipped_img.shape, img_tensor.shape)
         self.assertTrue(torch.equal(img_tensor, vflipped_img_again))
+        self.assertTrue(torch.equal(img_tensor, img_tensor_clone))
 
     def test_hflip(self):
         img_tensor = torch.randn(3, 16, 16)
+        img_tensor_clone = img_tensor.clone()
         hflipped_img = F_t.hflip(img_tensor)
         hflipped_img_again = F_t.hflip(hflipped_img)
         self.assertEqual(hflipped_img.shape, img_tensor.shape)
         self.assertTrue(torch.equal(img_tensor, hflipped_img_again))
+        self.assertTrue(torch.equal(img_tensor, img_tensor_clone))
 
     def test_crop(self):
         img_tensor = torch.randint(0, 255, (3, 16, 16), dtype=torch.uint8)
+        img_tensor_clone = img_tensor.clone()
         top = random.randint(0, 15)
         left = random.randint(0, 15)
         height = random.randint(1, 16 - top)
@@ -34,7 +39,7 @@ def test_crop(self):
         img_PIL = transforms.ToPILImage()(img_tensor)
         img_PIL_cropped = F.crop(img_PIL, top, left, height, width)
         img_cropped_GT = transforms.ToTensor()(img_PIL_cropped)
-
+        self.assertTrue(torch.equal(img_tensor, img_tensor_clone))
         self.assertTrue(torch.equal(img_cropped, (img_cropped_GT * 255).to(torch.uint8)),
                         "functional_tensor crop not working")
 
@@ -54,6 +59,7 @@ def test_adjustments(self):
                 img = torch.randint(0, 256, shape, dtype=torch.uint8)
 
             factor = 3 * torch.rand(1)
+            img_clone = img.clone()
             for f, ft in fns:
 
                 ft_img = ft(img, factor)
@@ -68,23 +74,29 @@ def test_adjustments(self):
                 # difference in values caused by (at most 5) truncations.
                 max_diff = (ft_img - f_img).abs().max()
                 self.assertLess(max_diff, 5 / 255 + 1e-5)
+                self.assertTrue(torch.equal(img, img_clone))
 
     def test_rgb_to_grayscale(self):
         img_tensor = torch.randint(0, 255, (3, 16, 16), dtype=torch.uint8)
+        img_tensor_clone = img_tensor.clone()
         grayscale_tensor = F_t.rgb_to_grayscale(img_tensor).to(int)
         grayscale_pil_img = torch.tensor(np.array(F.to_grayscale(F.to_pil_image(img_tensor)))).to(int)
         max_diff = (grayscale_tensor - grayscale_pil_img).abs().max()
         self.assertLess(max_diff, 1.0001)
+        self.assertTrue(torch.equal(img_tensor, img_tensor_clone))
 
     def test_center_crop(self):
         img_tensor = torch.randint(0, 255, (1, 32, 32), dtype=torch.uint8)
+        img_tensor_clone = img_tensor.clone()
         cropped_tensor = F_t.center_crop(img_tensor, [10, 10])
         cropped_pil_image = F.center_crop(transforms.ToPILImage()(img_tensor), [10, 10])
         cropped_pil_tensor = (transforms.ToTensor()(cropped_pil_image) * 255).to(torch.uint8)
         self.assertTrue(torch.equal(cropped_tensor, cropped_pil_tensor))
+        self.assertTrue(torch.equal(img_tensor, img_tensor_clone))
 
     def test_five_crop(self):
         img_tensor = torch.randint(0, 255, (1, 32, 32), dtype=torch.uint8)
+        img_tensor_clone = img_tensor.clone()
         cropped_tensor = F_t.five_crop(img_tensor, [10, 10])
         cropped_pil_image = F.five_crop(transforms.ToPILImage()(img_tensor), [10, 10])
         self.assertTrue(torch.equal(cropped_tensor[0],
@@ -97,9 +109,11 @@ def test_five_crop(self):
                                     (transforms.ToTensor()(cropped_pil_image[3]) * 255).to(torch.uint8)))
         self.assertTrue(torch.equal(cropped_tensor[4],
                                     (transforms.ToTensor()(cropped_pil_image[4]) * 255).to(torch.uint8)))
+        self.assertTrue(torch.equal(img_tensor, img_tensor_clone))
 
     def test_ten_crop(self):
         img_tensor = torch.randint(0, 255, (1, 32, 32), dtype=torch.uint8)
+        img_tensor_clone = img_tensor.clone()
         cropped_tensor = F_t.ten_crop(img_tensor, [10, 10])
         cropped_pil_image = F.ten_crop(transforms.ToPILImage()(img_tensor), [10, 10])
         self.assertTrue(torch.equal(cropped_tensor[0],
@@ -122,6 +136,7 @@ def test_ten_crop(self):
                                     (transforms.ToTensor()(cropped_pil_image[8]) * 255).to(torch.uint8)))
         self.assertTrue(torch.equal(cropped_tensor[9],
                                     (transforms.ToTensor()(cropped_pil_image[9]) * 255).to(torch.uint8)))
+        self.assertTrue(torch.equal(img_tensor, img_tensor_clone))
 
 
 if __name__ == '__main__':