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AdjustBrightness/Contrast/Hue/Saturation CV-CUDA Backend #9297

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44db71c
implement additional cvcuda infra for all branches to avoid duplicate…
justincdavis Nov 25, 2025
e3dd700
update make_image_cvcuda to have default batch dim
justincdavis Nov 25, 2025
c035df1
add stanardized setup to main for easier updating of PRs and branches
justincdavis Dec 2, 2025
98d7dfb
update is_cvcuda_tensor
justincdavis Dec 2, 2025
ddc116d
add cvcuda to pil compatible to transforms by default
justincdavis Dec 2, 2025
e51dc7e
remove cvcuda from transform class
justincdavis Dec 2, 2025
e14e210
merge with main
justincdavis Dec 4, 2025
4939355
resolve more formatting naming
justincdavis Dec 4, 2025
fbea584
update is cvcuda tensor impl
justincdavis Dec 4, 2025
511c169
adjust brightness done and tested
justincdavis Nov 25, 2025
54f3f4a
completed and tested adjust_contrast
justincdavis Nov 26, 2025
b11c38a
update brightness contrast tests plus add comment on mean calc for co…
justincdavis Dec 2, 2025
d379658
complete and tested adjust_hue
justincdavis Nov 26, 2025
310982c
merge brightness contrast and hue adjustment together
justincdavis Dec 2, 2025
e0392a0
wip adjust_saturation
justincdavis Nov 25, 2025
61b237c
adjust saturation complete and tested
justincdavis Nov 25, 2025
2c68fc3
add adjust saturation
justincdavis Dec 2, 2025
8564f0a
update to main standards
justincdavis Dec 4, 2025
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157 changes: 143 additions & 14 deletions test/test_transforms_v2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,6 +21,7 @@
import torchvision.transforms.v2 as transforms

from common_utils import (
assert_close,
assert_equal,
cache,
cpu_and_cuda,
Expand All @@ -41,7 +42,6 @@
)

from torch import nn
from torch.testing import assert_close
from torch.utils._pytree import tree_flatten, tree_map
from torch.utils.data import DataLoader, default_collate
from torchvision import tv_tensors
Expand Down Expand Up @@ -2822,7 +2822,18 @@ class TestAdjustBrightness:
def test_kernel(self, kernel, make_input, dtype, device):
check_kernel(kernel, make_input(dtype=dtype, device=device), brightness_factor=self._DEFAULT_BRIGHTNESS_FACTOR)

@pytest.mark.parametrize("make_input", [make_image_tensor, make_image_pil, make_image, make_video])
@pytest.mark.parametrize(
"make_input",
[
make_image_tensor,
make_image_pil,
make_image,
make_video,
pytest.param(
make_image_cvcuda, marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="CVCUDA not available")
),
],
)
def test_functional(self, make_input):
check_functional(F.adjust_brightness, make_input(), brightness_factor=self._DEFAULT_BRIGHTNESS_FACTOR)

Expand All @@ -2833,19 +2844,42 @@ def test_functional(self, make_input):
(F._color._adjust_brightness_image_pil, PIL.Image.Image),
(F.adjust_brightness_image, tv_tensors.Image),
(F.adjust_brightness_video, tv_tensors.Video),
pytest.param(
F._color._adjust_brightness_image_cvcuda,
None,
marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="CVCUDA not available"),
),
],
)
def test_functional_signature(self, kernel, input_type):
if kernel is F._color._adjust_brightness_image_cvcuda:
input_type = _import_cvcuda().Tensor
check_functional_kernel_signature_match(F.adjust_brightness, kernel=kernel, input_type=input_type)

@pytest.mark.parametrize(
"make_input",
[
make_image,
pytest.param(
make_image_cvcuda, marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="CVCUDA not available")
),
],
)
@pytest.mark.parametrize("brightness_factor", _CORRECTNESS_BRIGHTNESS_FACTORS)
def test_image_correctness(self, brightness_factor):
image = make_image(dtype=torch.uint8, device="cpu")
def test_image_correctness(self, make_input, brightness_factor):
image = make_input(dtype=torch.uint8, device="cpu")

actual = F.adjust_brightness(image, brightness_factor=brightness_factor)

if make_input is make_image_cvcuda:
image = F.cvcuda_to_tensor(image)[0].cpu()

expected = F.to_image(F.adjust_brightness(F.to_pil_image(image), brightness_factor=brightness_factor))

torch.testing.assert_close(actual, expected)
if make_input is make_image_cvcuda:
assert_close(actual, expected, rtol=0, atol=1)
else:
assert_close(actual, expected)


class TestCutMixMixUp:
Expand Down Expand Up @@ -6053,7 +6087,18 @@ def test_kernel_image(self, dtype, device):
def test_kernel_video(self):
check_kernel(F.adjust_contrast_video, make_video(), contrast_factor=0.5)

@pytest.mark.parametrize("make_input", [make_image_tensor, make_image, make_image_pil, make_video])
@pytest.mark.parametrize(
"make_input",
[
make_image_tensor,
make_image,
make_image_pil,
make_video,
pytest.param(
make_image_cvcuda, marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="CVCUDA not available")
),
],
)
def test_functional(self, make_input):
check_functional(F.adjust_contrast, make_input(), contrast_factor=0.5)

Expand All @@ -6064,9 +6109,16 @@ def test_functional(self, make_input):
(F._color._adjust_contrast_image_pil, PIL.Image.Image),
(F.adjust_contrast_image, tv_tensors.Image),
(F.adjust_contrast_video, tv_tensors.Video),
pytest.param(
F._color._adjust_contrast_image_cvcuda,
None,
marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="CVCUDA not available"),
),
],
)
def test_functional_signature(self, kernel, input_type):
if kernel is F._color._adjust_contrast_image_cvcuda:
input_type = _import_cvcuda().Tensor
check_functional_kernel_signature_match(F.adjust_contrast, kernel=kernel, input_type=input_type)

def test_functional_error(self):
Expand All @@ -6076,11 +6128,24 @@ def test_functional_error(self):
with pytest.raises(ValueError, match="is not non-negative"):
F.adjust_contrast(make_image(), contrast_factor=-1)

@pytest.mark.parametrize(
"make_input",
[
make_image,
pytest.param(
make_image_cvcuda, marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="CVCUDA not available")
),
],
)
@pytest.mark.parametrize("contrast_factor", [0.1, 0.5, 1.0])
def test_correctness_image(self, contrast_factor):
image = make_image(dtype=torch.uint8, device="cpu")
def test_correctness_image(self, make_input, contrast_factor):
image = make_input(dtype=torch.uint8, device="cpu")

actual = F.adjust_contrast(image, contrast_factor=contrast_factor)

if make_input is make_image_cvcuda:
image = F.cvcuda_to_tensor(image)[0].cpu()

expected = F.to_image(F.adjust_contrast(F.to_pil_image(image), contrast_factor=contrast_factor))

assert_close(actual, expected, rtol=0, atol=1)
Expand Down Expand Up @@ -6135,7 +6200,18 @@ def test_kernel_image(self, dtype, device):
def test_kernel_video(self):
check_kernel(F.adjust_hue_video, make_video(), hue_factor=0.25)

@pytest.mark.parametrize("make_input", [make_image_tensor, make_image, make_image_pil, make_video])
@pytest.mark.parametrize(
"make_input",
[
make_image_tensor,
make_image,
make_image_pil,
make_video,
pytest.param(
make_image_cvcuda, marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="CVCUDA not available")
),
],
)
def test_functional(self, make_input):
check_functional(F.adjust_hue, make_input(), hue_factor=0.25)

Expand All @@ -6146,9 +6222,16 @@ def test_functional(self, make_input):
(F._color._adjust_hue_image_pil, PIL.Image.Image),
(F.adjust_hue_image, tv_tensors.Image),
(F.adjust_hue_video, tv_tensors.Video),
pytest.param(
F._color._adjust_hue_image_cvcuda,
None,
marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="CVCUDA not available"),
),
],
)
def test_functional_signature(self, kernel, input_type):
if kernel is F._color._adjust_hue_image_cvcuda:
input_type = _import_cvcuda().Tensor
check_functional_kernel_signature_match(F.adjust_hue, kernel=kernel, input_type=input_type)

def test_functional_error(self):
Expand All @@ -6159,11 +6242,25 @@ def test_functional_error(self):
with pytest.raises(ValueError, match=re.escape("is not in [-0.5, 0.5]")):
F.adjust_hue(make_image(), hue_factor=hue_factor)

@pytest.mark.parametrize(
"make_input",
[
make_image,
pytest.param(
make_image_cvcuda, marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="CVCUDA not available")
),
],
)
@pytest.mark.parametrize("hue_factor", [-0.5, -0.3, 0.0, 0.2, 0.5])
def test_correctness_image(self, hue_factor):
image = make_image(dtype=torch.uint8, device="cpu")
def test_correctness_image(self, make_input, hue_factor):
image = make_input(dtype=torch.uint8, device="cpu")

actual = F.adjust_hue(image, hue_factor=hue_factor)

if make_input is make_image_cvcuda:
actual = F.cvcuda_to_tensor(actual)[0].cpu()
image = F.cvcuda_to_tensor(image)[0].cpu()

expected = F.to_image(F.adjust_hue(F.to_pil_image(image), hue_factor=hue_factor))

mae = (actual.float() - expected.float()).abs().mean()
Expand All @@ -6179,7 +6276,18 @@ def test_kernel_image(self, dtype, device):
def test_kernel_video(self):
check_kernel(F.adjust_saturation_video, make_video(), saturation_factor=0.5)

@pytest.mark.parametrize("make_input", [make_image_tensor, make_image, make_image_pil, make_video])
@pytest.mark.parametrize(
"make_input",
[
make_image_tensor,
make_image,
make_image_pil,
make_video,
pytest.param(
make_image_cvcuda, marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="CVCUDA not available")
),
],
)
def test_functional(self, make_input):
check_functional(F.adjust_saturation, make_input(), saturation_factor=0.5)

Expand All @@ -6190,9 +6298,16 @@ def test_functional(self, make_input):
(F._color._adjust_saturation_image_pil, PIL.Image.Image),
(F.adjust_saturation_image, tv_tensors.Image),
(F.adjust_saturation_video, tv_tensors.Video),
pytest.param(
F._color._adjust_saturation_image_cvcuda,
None,
marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="CVCUDA not available"),
),
],
)
def test_functional_signature(self, kernel, input_type):
if kernel is F._color._adjust_saturation_image_cvcuda:
input_type = _import_cvcuda().Tensor
check_functional_kernel_signature_match(F.adjust_saturation, kernel=kernel, input_type=input_type)

def test_functional_error(self):
Expand All @@ -6202,11 +6317,25 @@ def test_functional_error(self):
with pytest.raises(ValueError, match="is not non-negative"):
F.adjust_saturation(make_image(), saturation_factor=-1)

@pytest.mark.parametrize(
"make_input",
[
make_image,
pytest.param(
make_image_cvcuda, marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="CVCUDA not available")
),
],
)
@pytest.mark.parametrize("color_space", ["RGB", "GRAY"])
@pytest.mark.parametrize("saturation_factor", [0.1, 0.5, 1.0])
def test_correctness_image(self, saturation_factor):
image = make_image(dtype=torch.uint8, device="cpu")
def test_correctness_image(self, make_input, color_space, saturation_factor):
image = make_input(dtype=torch.uint8, color_space=color_space, device="cpu")

actual = F.adjust_saturation(image, saturation_factor=saturation_factor)

if make_input is make_image_cvcuda:
image = F.cvcuda_to_tensor(image)[0].cpu()

expected = F.to_image(F.adjust_saturation(F.to_pil_image(image), saturation_factor=saturation_factor))

assert_close(actual, expected, rtol=0, atol=1)
Expand Down
5 changes: 3 additions & 2 deletions torchvision/transforms/v2/_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@

from torchvision.transforms.transforms import _check_sequence_input, _setup_angle, _setup_size # noqa: F401
from torchvision.transforms.v2.functional import get_dimensions, get_size, is_pure_tensor
from torchvision.transforms.v2.functional._utils import _FillType, _FillTypeJIT
from torchvision.transforms.v2.functional._utils import _FillType, _FillTypeJIT, _is_cvcuda_tensor


def _setup_number_or_seq(arg: int | float | Sequence[int | float], name: str) -> Sequence[float]:
Expand Down Expand Up @@ -182,7 +182,7 @@ def query_chw(flat_inputs: list[Any]) -> tuple[int, int, int]:
chws = {
tuple(get_dimensions(inpt))
for inpt in flat_inputs
if check_type(inpt, (is_pure_tensor, tv_tensors.Image, PIL.Image.Image, tv_tensors.Video))
if check_type(inpt, (is_pure_tensor, tv_tensors.Image, PIL.Image.Image, tv_tensors.Video, _is_cvcuda_tensor))
}
if not chws:
raise TypeError("No image or video was found in the sample")
Expand All @@ -207,6 +207,7 @@ def query_size(flat_inputs: list[Any]) -> tuple[int, int]:
tv_tensors.Mask,
tv_tensors.BoundingBoxes,
tv_tensors.KeyPoints,
_is_cvcuda_tensor,
),
)
}
Expand Down
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