diff --git a/test/test_transforms.py b/test/test_transforms.py
index e86b6959517..f12678e1d5f 100644
--- a/test/test_transforms.py
+++ b/test/test_transforms.py
@@ -269,7 +269,7 @@ def test_pil_to_tensor(self, channels):
         input_data = torch.as_tensor(np.random.rand(channels, height, width).astype(np.float32))
         img = transforms.ToPILImage()(input_data)  # CHW -> HWC and (* 255).byte()
         output = trans(img)  # HWC -> CHW
-        expected_output = (input_data * 255).byte()
+        expected_output = (input_data * 255).round().byte()
         torch.testing.assert_close(output, expected_output, check_stride=False)
 
         # separate test for mode '1' PIL images
@@ -502,6 +502,7 @@ def test_pad_with_mode_F_images(self):
 
         img = Image.new("F", (10, 10))
         padded_img = transform(img)
+
         assert_equal(padded_img.size, [edge_size + 2 * pad for edge_size in img.size], check_stride=False)
 
 
@@ -539,7 +540,7 @@ class TestToPil:
 
     def _get_1_channel_tensor_various_types():
         img_data_float = torch.Tensor(1, 4, 4).uniform_()
-        expected_output = img_data_float.mul(255).int().float().div(255).numpy()
+        expected_output = img_data_float.mul(255).round().div(255).numpy()
         yield img_data_float, expected_output, 'L'
 
         img_data_byte = torch.ByteTensor(1, 4, 4).random_(0, 255)
@@ -556,7 +557,7 @@ def _get_1_channel_tensor_various_types():
 
     def _get_2d_tensor_various_types():
         img_data_float = torch.Tensor(4, 4).uniform_()
-        expected_output = img_data_float.mul(255).int().float().div(255).numpy()
+        expected_output = img_data_float.mul(255).round().div(255).numpy()
         yield img_data_float, expected_output, 'L'
 
         img_data_byte = torch.ByteTensor(4, 4).random_(0, 255)
@@ -634,7 +635,7 @@ def test_2_channel_ndarray_to_pil_image_error(self):
     @pytest.mark.parametrize('expected_mode', [None, 'LA'])
     def test_2_channel_tensor_to_pil_image(self, expected_mode):
         img_data = torch.Tensor(2, 4, 4).uniform_()
-        expected_output = img_data.mul(255).int().float().div(255)
+        expected_output = img_data.mul(255).round().div(255)
         if expected_mode is None:
             img = transforms.ToPILImage()(img_data)
             assert img.mode == 'LA'  # default should assume LA
@@ -683,7 +684,7 @@ def test_2d_ndarray_to_pil_image(self, with_mode, img_data, expected_mode):
     @pytest.mark.parametrize('expected_mode', [None, 'RGB', 'HSV', 'YCbCr'])
     def test_3_channel_tensor_to_pil_image(self, expected_mode):
         img_data = torch.Tensor(3, 4, 4).uniform_()
-        expected_output = img_data.mul(255).int().float().div(255)
+        expected_output = img_data.mul(255).round().div(255)
 
         if expected_mode is None:
             img = transforms.ToPILImage()(img_data)
@@ -741,7 +742,7 @@ def test_3_channel_ndarray_to_pil_image_error(self):
     @pytest.mark.parametrize('expected_mode', [None, 'RGBA', 'CMYK', 'RGBX'])
     def test_4_channel_tensor_to_pil_image(self, expected_mode):
         img_data = torch.Tensor(4, 4, 4).uniform_()
-        expected_output = img_data.mul(255).int().float().div(255)
+        expected_output = img_data.mul(255).round().div(255)
 
         if expected_mode is None:
             img = transforms.ToPILImage()(img_data)
@@ -815,6 +816,25 @@ def test_tensor_bad_types_to_pil_image(self):
         with pytest.raises(ValueError, match=r'pic should not have > 4 channels. Got \d+ channels.'):
             transforms.ToPILImage()(torch.ones(6, 4, 4))
 
+    @pytest.mark.parametrize('input_img, expected_output', [
+        (torch.full((4, 4), 1.01), np.full((4, 4), 255)),
+        (torch.full((4, 4), -0.01), np.full((4, 4), 0))
+    ])
+    def test_tensor_to_pil_no_overshoot(self, input_img, expected_output):
+        transform = transforms.ToPILImage()
+        img = transform(input_img)
+        torch.testing.assert_close(expected_output, np.array(img), check_dtype=False)
+
+    def test_tensor_to_pil_robust(self, input_img, expected_output):
+        to_pil = transforms.ToPILImage()
+        to_tensor = transforms.ToTensor()
+        input_img = torch.ByteTensor(20, 20).random_(0, 255).numpy()
+        eps = 1.0e-5
+        img_sub = to_tensor(to_pil(input_img - eps))
+        torch.testing.assert_close(input_img, np.array(img_sub), check_dtype=False)
+        img_add = to_tensor(to_pil(input_img + eps))
+        torch.testing.assert_close(input_img, np.array(img_add), check_dtype=False)
+
 
 def test_adjust_brightness():
     x_shape = [2, 2, 3]
diff --git a/torchvision/transforms/functional.py b/torchvision/transforms/functional.py
index 21f5c654f99..d9e68f51c72 100644
--- a/torchvision/transforms/functional.py
+++ b/torchvision/transforms/functional.py
@@ -241,7 +241,7 @@ def to_pil_image(pic, mode=None):
     npimg = pic
     if isinstance(pic, torch.Tensor):
         if pic.is_floating_point() and mode != 'F':
-            pic = pic.mul(255).byte()
+            pic = pic.clamp(0, 1).mul(255).round().byte()
         npimg = np.transpose(pic.cpu().numpy(), (1, 2, 0))
 
     if not isinstance(npimg, np.ndarray):