Add GPU support for draw_segmentation_masks

test/test_utils.py

@@ -9,7 +9,7 @@
 import torch
 import torchvision.transforms.functional as F
 import torchvision.utils as utils
-from common_utils import assert_equal
+from common_utils import assert_equal, cpu_and_cuda
 from PIL import __version__ as PILLOW_VERSION, Image, ImageColor
 
 
@@ -203,12 +203,13 @@ def test_draw_no_boxes():
     ],
 )
 @pytest.mark.parametrize("alpha", (0, 0.5, 0.7, 1))
-def test_draw_segmentation_masks(colors, alpha):
+@pytest.mark.parametrize("device", cpu_and_cuda())
+def test_draw_segmentation_masks(colors, alpha, device):
     """This test makes sure that masks draw their corresponding color where they should"""
     num_masks, h, w = 2, 100, 100
     dtype = torch.uint8
-    img = torch.randint(0, 256, size=(3, h, w), dtype=dtype)
-    masks = torch.randint(0, 2, (num_masks, h, w), dtype=torch.bool)
+    img = torch.randint(0, 256, size=(3, h, w), dtype=dtype, device=device)
+    masks = torch.randint(0, 2, (num_masks, h, w), dtype=torch.bool, device=device)
 
     # For testing we enforce that there's no overlap between the masks. The
     # current behaviour is that the last mask's color will take priority when
@@ -234,7 +235,7 @@ def test_draw_segmentation_masks(colors, alpha):
     for mask, color in zip(masks, colors):
         if isinstance(color, str):
             color = ImageColor.getrgb(color)
-        color = torch.tensor(color, dtype=dtype)
+        color = torch.tensor(color, dtype=dtype, device=device)
 
         if alpha == 1:
             assert (out[:, mask] == color[:, None]).all()
@@ -245,11 +246,12 @@ def test_draw_segmentation_masks(colors, alpha):
         torch.testing.assert_close(out[:, mask], interpolated_color, rtol=0.0, atol=1.0)
 
 
-def test_draw_segmentation_masks_errors():
+@pytest.mark.parametrize("device", cpu_and_cuda())
+def test_draw_segmentation_masks_errors(device):
     h, w = 10, 10
 
-    masks = torch.randint(0, 2, size=(h, w), dtype=torch.bool)
-    img = torch.randint(0, 256, size=(3, h, w), dtype=torch.uint8)
+    masks = torch.randint(0, 2, size=(h, w), dtype=torch.bool, device=device)
+    img = torch.randint(0, 256, size=(3, h, w), dtype=torch.uint8, device=device)
 
     with pytest.raises(TypeError, match="The image must be a tensor"):
         utils.draw_segmentation_masks(image="Not A Tensor Image", masks=masks)
@@ -281,9 +283,10 @@ def test_draw_segmentation_masks_errors():
         utils.draw_segmentation_masks(image=img, masks=masks, colors=bad_colors)
 
 
-def test_draw_no_segmention_mask():
-    img = torch.full((3, 100, 100), 0, dtype=torch.uint8)
-    masks = torch.full((0, 100, 100), 0, dtype=torch.bool)
+@pytest.mark.parametrize("device", cpu_and_cuda())
+def test_draw_no_segmention_mask(device):
+    img = torch.full((3, 100, 100), 0, dtype=torch.uint8, device=device)
+    masks = torch.full((0, 100, 100), 0, dtype=torch.bool, device=device)
     with pytest.warns(UserWarning, match=re.escape("masks doesn't contain any mask. No mask was drawn")):
         res = utils.draw_segmentation_masks(img, masks)
         # Check that the function didn't change the image

torchvision/utils.py

@@ -304,7 +304,7 @@ def draw_segmentation_masks(
         return image
 
     out_dtype = torch.uint8
-    colors = [torch.tensor(color, dtype=out_dtype) for color in _parse_colors(colors, num_objects=num_masks)]
+    colors = [torch.tensor(color, dtype=out_dtype, device=image.device) for color in _parse_colors(colors, num_objects=num_masks)]
 
     img_to_draw = image.detach().clone()
     # TODO: There might be a way to vectorize this