Update functional_tensor.py

test/test_functional_tensor.py

@@ -795,6 +795,36 @@ def test_solarize2(device, dtype, config, channels):
     )
 
 
+@pytest.mark.parametrize("device", cpu_and_gpu())
+@pytest.mark.parametrize("threshold", [0.0, 0.25, 0.5, 0.75, 1.0])
+def test_solarize_threshold1_bound(threshold, device):
+    img = torch.rand((3, 12, 23)).to(device)
+    F_t.solarize(img, threshold)
+
+
+@pytest.mark.parametrize("device", cpu_and_gpu())
+@pytest.mark.parametrize("threshold", [1.5])
+def test_solarize_threshold1_upper_bound(threshold, device):
+    img = torch.rand((3, 12, 23)).to(device)
+    with pytest.raises(TypeError, match="Threshold should be less than bound of img."):
+        F_t.solarize(img, threshold)
+
+
+@pytest.mark.parametrize("device", cpu_and_gpu())
+@pytest.mark.parametrize("threshold", [0, 64, 128, 192, 255])
+def test_solarize_threshold2_bound(threshold, device):
+    img = torch.randint(0, 256, (3, 12, 23)).to(device)
+    F_t.solarize(img, threshold)
+
+
+@pytest.mark.parametrize("device", cpu_and_gpu())
+@pytest.mark.parametrize("threshold", [260])
+def test_solarize_threshold2_upper_bound(threshold, device):
+    img = torch.randint(0, 256, (3, 12, 23)).to(device)
+    with pytest.raises(TypeError, match="Threshold should be less than bound of img."):
+        F_t.solarize(img, threshold)
+
+
 @pytest.mark.parametrize("device", cpu_and_gpu())
 @pytest.mark.parametrize("dtype", (None, torch.float32, torch.float64))
 @pytest.mark.parametrize("config", [{"sharpness_factor": f} for f in [0.2, 0.5, 1.0, 1.5, 2.0]])

torchvision/transforms/functional_tensor.py

@@ -16,6 +16,12 @@ def _assert_image_tensor(img: Tensor) -> None:
         raise TypeError("Tensor is not a torch image.")
 
 
+def _assert_threshold(img: Tensor, threshold: float) -> None:
+    bound = 1 if img.is_floating_point() else 255
+    if threshold > bound:
+        raise TypeError("Threshold should be less than bound of img.")
+
+
 def get_image_size(img: Tensor) -> List[int]:
     # Returns (w, h) of tensor image
     _assert_image_tensor(img)
@@ -882,6 +888,8 @@ def solarize(img: Tensor, threshold: float) -> Tensor:
 
     _assert_channels(img, [1, 3])
 
+    _assert_threshold(img, threshold)
+
     inverted_img = invert(img)
     return torch.where(img >= threshold, inverted_img, img)