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justincdavisjustincdavis
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affine implemented and passing tests
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+138
-6
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2 files changed

+138
-6
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‎test/test_transforms_v2.py‎

Lines changed: 63 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -1513,6 +1513,9 @@ def test_kernel_video(self):
15131513
make_segmentation_mask,
15141514
make_video,
15151515
make_keypoints,
1516+
pytest.param(
1517+
make_image_cvcuda, marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="CVCUDA not available")
1518+
),
15161519
],
15171520
)
15181521
def test_functional(self, make_input):
@@ -1528,9 +1531,16 @@ def test_functional(self, make_input):
15281531
(F.affine_mask, tv_tensors.Mask),
15291532
(F.affine_video, tv_tensors.Video),
15301533
(F.affine_keypoints, tv_tensors.KeyPoints),
1534+
pytest.param(
1535+
F._geometry._affine_cvcuda,
1536+
"cvcuda.Tensor",
1537+
marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="CVCUDA not available"),
1538+
),
15311539
],
15321540
)
15331541
def test_functional_signature(self, kernel, input_type):
1542+
if input_type == "cvcuda.Tensor":
1543+
input_type = _import_cvcuda().Tensor
15341544
check_functional_kernel_signature_match(F.affine, kernel=kernel, input_type=input_type)
15351545

15361546
@pytest.mark.parametrize(
@@ -1543,6 +1553,9 @@ def test_functional_signature(self, kernel, input_type):
15431553
make_segmentation_mask,
15441554
make_video,
15451555
make_keypoints,
1556+
pytest.param(
1557+
make_image_cvcuda, marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="CVCUDA not available")
1558+
),
15461559
],
15471560
)
15481561
@pytest.mark.parametrize("device", cpu_and_cuda())
@@ -1560,8 +1573,19 @@ def test_transform(self, make_input, device):
15601573
"interpolation", [transforms.InterpolationMode.NEAREST, transforms.InterpolationMode.BILINEAR]
15611574
)
15621575
@pytest.mark.parametrize("fill", CORRECTNESS_FILLS)
1563-
def test_functional_image_correctness(self, angle, translate, scale, shear, center, interpolation, fill):
1564-
image = make_image(dtype=torch.uint8, device="cpu")
1576+
@pytest.mark.parametrize(
1577+
"make_input",
1578+
[
1579+
make_image,
1580+
pytest.param(
1581+
make_image_cvcuda, marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="CVCUDA not available")
1582+
),
1583+
],
1584+
)
1585+
def test_functional_image_correctness(
1586+
self, angle, translate, scale, shear, center, interpolation, fill, make_input
1587+
):
1588+
image = make_input(dtype=torch.uint8, device="cpu")
15651589

15661590
fill = adapt_fill(fill, dtype=torch.uint8)
15671591

@@ -1575,6 +1599,14 @@ def test_functional_image_correctness(self, angle, translate, scale, shear, cent
15751599
interpolation=interpolation,
15761600
fill=fill,
15771601
)
1602+
1603+
if make_input is make_image_cvcuda:
1604+
actual = F.cvcuda_to_tensor(actual).to(device="cpu")
1605+
actual = actual.squeeze(0)
1606+
# drop the batch dimensions for image now
1607+
image = F.cvcuda_to_tensor(image)
1608+
image = image.squeeze(0)
1609+
15781610
expected = F.to_image(
15791611
F.affine(
15801612
F.to_pil_image(image),
@@ -1589,16 +1621,29 @@ def test_functional_image_correctness(self, angle, translate, scale, shear, cent
15891621
)
15901622

15911623
mae = (actual.float() - expected.float()).abs().mean()
1592-
assert mae < 2 if interpolation is transforms.InterpolationMode.NEAREST else 8
1624+
if make_input is make_image_cvcuda:
1625+
# CV-CUDA nearest interpolation does not follow same algorithm as PIL/torch
1626+
assert mae < 255 if interpolation is transforms.InterpolationMode.NEAREST else 1, f"mae: {mae}"
1627+
else:
1628+
assert mae < 2 if interpolation is transforms.InterpolationMode.NEAREST else 8, f"mae: {mae}"
15931629

15941630
@pytest.mark.parametrize("center", _CORRECTNESS_AFFINE_KWARGS["center"])
15951631
@pytest.mark.parametrize(
15961632
"interpolation", [transforms.InterpolationMode.NEAREST, transforms.InterpolationMode.BILINEAR]
15971633
)
15981634
@pytest.mark.parametrize("fill", CORRECTNESS_FILLS)
15991635
@pytest.mark.parametrize("seed", list(range(5)))
1600-
def test_transform_image_correctness(self, center, interpolation, fill, seed):
1601-
image = make_image(dtype=torch.uint8, device="cpu")
1636+
@pytest.mark.parametrize(
1637+
"make_input",
1638+
[
1639+
make_image,
1640+
pytest.param(
1641+
make_image_cvcuda, marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="CVCUDA not available")
1642+
),
1643+
],
1644+
)
1645+
def test_transform_image_correctness(self, center, interpolation, fill, seed, make_input):
1646+
image = make_input(dtype=torch.uint8, device="cpu")
16021647

16031648
fill = adapt_fill(fill, dtype=torch.uint8)
16041649

@@ -1609,11 +1654,23 @@ def test_transform_image_correctness(self, center, interpolation, fill, seed):
16091654
torch.manual_seed(seed)
16101655
actual = transform(image)
16111656

1657+
if make_input is make_image_cvcuda:
1658+
actual = F.cvcuda_to_tensor(actual).to(device="cpu")
1659+
actual = actual.squeeze(0)
1660+
# drop the batch dimensions for image now
1661+
image = F.cvcuda_to_tensor(image)
1662+
image = image.squeeze(0)
1663+
16121664
torch.manual_seed(seed)
16131665
expected = F.to_image(transform(F.to_pil_image(image)))
16141666

16151667
mae = (actual.float() - expected.float()).abs().mean()
1616-
assert mae < 2 if interpolation is transforms.InterpolationMode.NEAREST else 8
1668+
mae = (actual.float() - expected.float()).abs().mean()
1669+
if make_input is make_image_cvcuda:
1670+
# CV-CUDA nearest interpolation does not follow same algorithm as PIL/torch
1671+
assert mae < 255 if interpolation is transforms.InterpolationMode.NEAREST else 1, f"mae: {mae}"
1672+
else:
1673+
assert mae < 2 if interpolation is transforms.InterpolationMode.NEAREST else 8, f"mae: {mae}"
16171674

16181675
def _compute_affine_matrix(self, *, angle, translate, scale, shear, center):
16191676
rot = math.radians(angle)

‎torchvision/transforms/v2/functional/_geometry.py‎

Lines changed: 75 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -4,6 +4,7 @@
44
from collections.abc import Sequence
55
from typing import Any, Optional, TYPE_CHECKING, Union
66

7+
import numpy as np
78
import PIL.Image
89
import torch
910
from torch.nn.functional import grid_sample, interpolate, pad as torch_pad
@@ -1331,6 +1332,80 @@ def affine_video(
13311332
)
13321333

13331334

1335+
if CVCUDA_AVAILABLE:
1336+
_cvcuda_interp = {
1337+
InterpolationMode.BILINEAR: cvcuda.Interp.LINEAR,
1338+
"bilinear": cvcuda.Interp.LINEAR,
1339+
"linear": cvcuda.Interp.LINEAR,
1340+
2: cvcuda.Interp.LINEAR,
1341+
InterpolationMode.BICUBIC: cvcuda.Interp.CUBIC,
1342+
"bicubic": cvcuda.Interp.CUBIC,
1343+
3: cvcuda.Interp.CUBIC,
1344+
InterpolationMode.NEAREST: cvcuda.Interp.NEAREST,
1345+
"nearest": cvcuda.Interp.NEAREST,
1346+
0: cvcuda.Interp.NEAREST,
1347+
InterpolationMode.BOX: cvcuda.Interp.BOX,
1348+
"box": cvcuda.Interp.BOX,
1349+
4: cvcuda.Interp.BOX,
1350+
InterpolationMode.HAMMING: cvcuda.Interp.HAMMING,
1351+
"hamming": cvcuda.Interp.HAMMING,
1352+
5: cvcuda.Interp.HAMMING,
1353+
InterpolationMode.LANCZOS: cvcuda.Interp.LANCZOS,
1354+
"lanczos": cvcuda.Interp.LANCZOS,
1355+
1: cvcuda.Interp.LANCZOS,
1356+
}
1357+
1358+
1359+
def _affine_cvcuda(
1360+
image: "cvcuda.Tensor",
1361+
angle: Union[int, float],
1362+
translate: list[float],
1363+
scale: float,
1364+
shear: list[float],
1365+
interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST,
1366+
fill: _FillTypeJIT = None,
1367+
center: Optional[list[float]] = None,
1368+
) -> "cvcuda.Tensor":
1369+
cvcuda = _import_cvcuda()
1370+
1371+
interpolation = _check_interpolation(interpolation)
1372+
angle, translate, shear, center = _affine_parse_args(angle, translate, scale, shear, interpolation, center)
1373+
1374+
height, width, num_channels = image.shape[1:]
1375+
1376+
center_f = [0.0, 0.0]
1377+
if center is not None:
1378+
center_f = [(c - s * 0.5) for c, s in zip(center, [width, height])]
1379+
1380+
translate_f = [float(t) for t in translate]
1381+
matrix = _get_inverse_affine_matrix(center_f, angle, translate_f, scale, shear)
1382+
1383+
interp = _cvcuda_interp.get(interpolation)
1384+
if interp is None:
1385+
raise ValueError(f"Invalid interpolation mode: {interpolation}")
1386+
1387+
xform = np.array([[matrix[0], matrix[1], matrix[2]], [matrix[3], matrix[4], matrix[5]]], dtype=np.float32)
1388+
1389+
if fill is None:
1390+
border_value = np.zeros(num_channels, dtype=np.float32)
1391+
elif isinstance(fill, (int, float)):
1392+
border_value = np.full(num_channels, fill, dtype=np.float32)
1393+
else:
1394+
border_value = np.array(fill, dtype=np.float32)[:num_channels]
1395+
1396+
return cvcuda.warp_affine(
1397+
image,
1398+
xform,
1399+
flags=interp | cvcuda.Interp.WARP_INVERSE_MAP,
1400+
border_mode=cvcuda.Border.CONSTANT,
1401+
border_value=border_value,
1402+
)
1403+
1404+
1405+
if CVCUDA_AVAILABLE:
1406+
_register_kernel_internal(affine, _import_cvcuda().Tensor)(_affine_cvcuda)
1407+
1408+
13341409
def rotate(
13351410
inpt: torch.Tensor,
13361411
angle: float,

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