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Unverified Commit ca012d39 authored by Philip Meier's avatar Philip Meier Committed by GitHub
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make PIL kernels private (#7831)

parent cdbbd666
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Showing with 329 additions and 355 deletions
docs/source/transforms.rst
View file @ ca012d39
......@@ -228,12 +228,11 @@ Conversion
ToPILImage
v2.ToPILImage
v2.ToImagePIL
ToTensor
v2.ToTensor
PILToTensor
v2.PILToTensor
v2.ToImageTensor
v2.ToImage
ConvertImageDtype
v2.ConvertImageDtype
v2.ToDtype
......
gallery/plot_transforms_v2_e2e.py
View file @ ca012d39
......@@ -27,7 +27,7 @@ def show(sample):
image, target = sample
if isinstance(image, PIL.Image.Image):
image = F.to_image_tensor(image)
image = F.to_image(image)
image = F.to_dtype(image, torch.uint8, scale=True)
annotated_image = draw_bounding_boxes(image, target["boxes"], colors="yellow", width=3)
......@@ -101,7 +101,7 @@ transform = transforms.Compose(
transforms.RandomZoomOut(fill={PIL.Image.Image: (123, 117, 104), "others": 0}),
transforms.RandomIoUCrop(),
transforms.RandomHorizontalFlip(),
transforms.ToImageTensor(),
transforms.ToImage(),
transforms.ConvertImageDtype(torch.float32),
transforms.SanitizeBoundingBoxes(),
]
......
references/detection/presets.py
View file @ ca012d39
......@@ -33,7 +33,7 @@ class DetectionPresetTrain:
transforms = []
backend = backend.lower()
if backend == "datapoint":
transforms.append(T.ToImageTensor())
transforms.append(T.ToImage())
elif backend == "tensor":
transforms.append(T.PILToTensor())
elif backend != "pil":
......@@ -71,7 +71,7 @@ class DetectionPresetTrain:
if backend == "pil":
# Note: we could just convert to pure tensors even in v2.
transforms += [T.ToImageTensor() if use_v2 else T.PILToTensor()]
transforms += [T.ToImage() if use_v2 else T.PILToTensor()]
transforms += [T.ConvertImageDtype(torch.float)]
......@@ -94,11 +94,11 @@ class DetectionPresetEval:
backend = backend.lower()
if backend == "pil":
# Note: we could just convert to pure tensors even in v2?
transforms += [T.ToImageTensor() if use_v2 else T.PILToTensor()]
transforms += [T.ToImage() if use_v2 else T.PILToTensor()]
elif backend == "tensor":
transforms += [T.PILToTensor()]
elif backend == "datapoint":
transforms += [T.ToImageTensor()]
transforms += [T.ToImage()]
else:
raise ValueError(f"backend can be 'datapoint', 'tensor' or 'pil', but got {backend}")
......
references/segmentation/presets.py
View file @ ca012d39
......@@ -32,7 +32,7 @@ class SegmentationPresetTrain:
transforms = []
backend = backend.lower()
if backend == "datapoint":
transforms.append(T.ToImageTensor())
transforms.append(T.ToImage())
elif backend == "tensor":
transforms.append(T.PILToTensor())
elif backend != "pil":
......@@ -81,7 +81,7 @@ class SegmentationPresetEval:
if backend == "tensor":
transforms += [T.PILToTensor()]
elif backend == "datapoint":
transforms += [T.ToImageTensor()]
transforms += [T.ToImage()]
elif backend != "pil":
raise ValueError(f"backend can be 'datapoint', 'tensor' or 'pil', but got {backend}")
......@@ -92,7 +92,7 @@ class SegmentationPresetEval:
if backend == "pil":
# Note: we could just convert to pure tensors even in v2?
transforms += [T.ToImageTensor() if use_v2 else T.PILToTensor()]
transforms += [T.ToImage() if use_v2 else T.PILToTensor()]
transforms += [
T.ConvertImageDtype(torch.float),
......
test/common_utils.py
View file @ ca012d39
......@@ -27,7 +27,7 @@ from PIL import Image
from torch.testing._comparison import BooleanPair, NonePair, not_close_error_metas, NumberPair, TensorLikePair
from torchvision import datapoints, io
from torchvision.transforms._functional_tensor import _max_value as get_max_value
from torchvision.transforms.v2.functional import to_dtype_image_tensor, to_image_pil, to_image_tensor
from torchvision.transforms.v2.functional import to_dtype_image, to_image, to_pil_image
IN_OSS_CI = any(os.getenv(var) == "true" for var in ["CIRCLECI", "GITHUB_ACTIONS"])
......@@ -293,7 +293,7 @@ class ImagePair(TensorLikePair):
**other_parameters,
):
if all(isinstance(input, PIL.Image.Image) for input in [actual, expected]):
actual, expected = [to_image_tensor(input) for input in [actual, expected]]
actual, expected = [to_image(input) for input in [actual, expected]]
super().__init__(actual, expected, **other_parameters)
self.mae = mae
......@@ -536,7 +536,7 @@ def make_image_tensor(*args, **kwargs):
def make_image_pil(*args, **kwargs):
return to_image_pil(make_image(*args, **kwargs))
return to_pil_image(make_image(*args, **kwargs))
def make_image_loader(
......@@ -609,12 +609,12 @@ def make_image_loader_for_interpolation(
)
)
image_tensor = to_image_tensor(image_pil)
image_tensor = to_image(image_pil)
if memory_format == torch.contiguous_format:
image_tensor = image_tensor.to(device=device, memory_format=memory_format, copy=True)
else:
image_tensor = image_tensor.to(device=device)
image_tensor = to_dtype_image_tensor(image_tensor, dtype=dtype, scale=True)
image_tensor = to_dtype_image(image_tensor, dtype=dtype, scale=True)
return datapoints.Image(image_tensor)
......
test/test_prototype_transforms.py
View file @ ca012d39
......@@ -17,7 +17,7 @@ from prototype_common_utils import make_label
from torchvision.datapoints import BoundingBoxes, BoundingBoxFormat, Image, Mask, Video
from torchvision.prototype import datapoints, transforms
from torchvision.transforms.v2.functional import clamp_bounding_boxes, InterpolationMode, pil_to_tensor, to_image_pil
from torchvision.transforms.v2.functional import clamp_bounding_boxes, InterpolationMode, pil_to_tensor, to_pil_image
from torchvision.transforms.v2.utils import check_type, is_simple_tensor
BATCH_EXTRA_DIMS = [extra_dims for extra_dims in DEFAULT_EXTRA_DIMS if extra_dims]
......@@ -387,7 +387,7 @@ def test_fixed_sized_crop_against_detection_reference():
size = (600, 800)
num_objects = 22
pil_image = to_image_pil(make_image(size=size, color_space="RGB"))
pil_image = to_pil_image(make_image(size=size, color_space="RGB"))
target = {
"boxes": make_bounding_box(canvas_size=size, format="XYXY", batch_dims=(num_objects,), dtype=torch.float),
"labels": make_label(extra_dims=(num_objects,), categories=80),
......
test/test_transforms_v2.py
View file @ ca012d39
......@@ -666,19 +666,19 @@ class TestTransform:
t(inpt)
class TestToImageTensor:
class TestToImage:
@pytest.mark.parametrize(
"inpt_type",
[torch.Tensor, PIL.Image.Image, datapoints.Image, np.ndarray, datapoints.BoundingBoxes, str, int],
)
def test__transform(self, inpt_type, mocker):
fn = mocker.patch(
"torchvision.transforms.v2.functional.to_image_tensor",
"torchvision.transforms.v2.functional.to_image",
return_value=torch.rand(1, 3, 8, 8),
)
inpt = mocker.MagicMock(spec=inpt_type)
transform = transforms.ToImageTensor()
transform = transforms.ToImage()
transform(inpt)
if inpt_type in (datapoints.BoundingBoxes, datapoints.Image, str, int):
assert fn.call_count == 0
......@@ -686,30 +686,13 @@ class TestToImageTensor:
fn.assert_called_once_with(inpt)
class TestToImagePIL:
@pytest.mark.parametrize(
"inpt_type",
[torch.Tensor, PIL.Image.Image, datapoints.Image, np.ndarray, datapoints.BoundingBoxes, str, int],
)
def test__transform(self, inpt_type, mocker):
fn = mocker.patch("torchvision.transforms.v2.functional.to_image_pil")
inpt = mocker.MagicMock(spec=inpt_type)
transform = transforms.ToImagePIL()
transform(inpt)
if inpt_type in (datapoints.BoundingBoxes, PIL.Image.Image, str, int):
assert fn.call_count == 0
else:
fn.assert_called_once_with(inpt, mode=transform.mode)
class TestToPILImage:
@pytest.mark.parametrize(
"inpt_type",
[torch.Tensor, PIL.Image.Image, datapoints.Image, np.ndarray, datapoints.BoundingBoxes, str, int],
)
def test__transform(self, inpt_type, mocker):
fn = mocker.patch("torchvision.transforms.v2.functional.to_image_pil")
fn = mocker.patch("torchvision.transforms.v2.functional.to_pil_image")
inpt = mocker.MagicMock(spec=inpt_type)
transform = transforms.ToPILImage()
......@@ -1013,7 +996,7 @@ def test_antialias_warning():
@pytest.mark.parametrize("image_type", (PIL.Image, torch.Tensor, datapoints.Image))
@pytest.mark.parametrize("label_type", (torch.Tensor, int))
@pytest.mark.parametrize("dataset_return_type", (dict, tuple))
@pytest.mark.parametrize("to_tensor", (transforms.ToTensor, transforms.ToImageTensor))
@pytest.mark.parametrize("to_tensor", (transforms.ToTensor, transforms.ToImage))
def test_classif_preset(image_type, label_type, dataset_return_type, to_tensor):
image = datapoints.Image(torch.randint(0, 256, size=(1, 3, 250, 250), dtype=torch.uint8))
......@@ -1074,7 +1057,7 @@ def test_classif_preset(image_type, label_type, dataset_return_type, to_tensor):
@pytest.mark.parametrize("image_type", (PIL.Image, torch.Tensor, datapoints.Image))
@pytest.mark.parametrize("data_augmentation", ("hflip", "lsj", "multiscale", "ssd", "ssdlite"))
@pytest.mark.parametrize("to_tensor", (transforms.ToTensor, transforms.ToImageTensor))
@pytest.mark.parametrize("to_tensor", (transforms.ToTensor, transforms.ToImage))
@pytest.mark.parametrize("sanitize", (True, False))
def test_detection_preset(image_type, data_augmentation, to_tensor, sanitize):
torch.manual_seed(0)
......
test/test_transforms_v2_consistency.py
View file @ ca012d39
......@@ -30,7 +30,7 @@ from torchvision._utils import sequence_to_str
from torchvision.transforms import functional as legacy_F
from torchvision.transforms.v2 import functional as prototype_F
from torchvision.transforms.v2._utils import _get_fill
from torchvision.transforms.v2.functional import to_image_pil
from torchvision.transforms.v2.functional import to_pil_image
from torchvision.transforms.v2.utils import query_size
DEFAULT_MAKE_IMAGES_KWARGS = dict(color_spaces=["RGB"], extra_dims=[(4,)])
......@@ -630,7 +630,7 @@ def check_call_consistency(
)
if image.ndim == 3 and supports_pil:
image_pil = to_image_pil(image)
image_pil = to_pil_image(image)
try:
torch.manual_seed(0)
......@@ -869,7 +869,7 @@ class TestToTensorTransforms:
legacy_transform = legacy_transforms.PILToTensor()
for image in make_images(extra_dims=[()]):
image_pil = to_image_pil(image)
image_pil = to_pil_image(image)
assert_equal(prototype_transform(image_pil), legacy_transform(image_pil))
......@@ -879,7 +879,7 @@ class TestToTensorTransforms:
legacy_transform = legacy_transforms.ToTensor()
for image in make_images(extra_dims=[()]):
image_pil = to_image_pil(image)
image_pil = to_pil_image(image)
image_numpy = np.array(image_pil)
assert_equal(prototype_transform(image_pil), legacy_transform(image_pil))
......@@ -1088,7 +1088,7 @@ class TestRefDetTransforms:
def make_label(extra_dims, categories):
return torch.randint(categories, extra_dims, dtype=torch.int64)
pil_image = to_image_pil(make_image(size=size, color_space="RGB"))
pil_image = to_pil_image(make_image(size=size, color_space="RGB"))
target = {
"boxes": make_bounding_box(canvas_size=size, format="XYXY", batch_dims=(num_objects,), dtype=torch.float),
"labels": make_label(extra_dims=(num_objects,), categories=80),
......@@ -1192,7 +1192,7 @@ class TestRefSegTransforms:
conv_fns = []
if supports_pil:
conv_fns.append(to_image_pil)
conv_fns.append(to_pil_image)
conv_fns.extend([torch.Tensor, lambda x: x])
for conv_fn in conv_fns:
......@@ -1201,8 +1201,8 @@ class TestRefSegTransforms:
dp = (conv_fn(datapoint_image), datapoint_mask)
dp_ref = (
to_image_pil(datapoint_image) if supports_pil else datapoint_image.as_subclass(torch.Tensor),
to_image_pil(datapoint_mask),
to_pil_image(datapoint_image) if supports_pil else datapoint_image.as_subclass(torch.Tensor),
to_pil_image(datapoint_mask),
)
yield dp, dp_ref
......
test/test_transforms_v2_functional.py
View file @ ca012d39
......@@ -280,12 +280,12 @@ class TestKernels:
adapted_other_args, adapted_kwargs = info.float32_vs_uint8(other_args, kwargs)
actual = info.kernel(
F.to_dtype_image_tensor(input, dtype=torch.float32, scale=True),
F.to_dtype_image(input, dtype=torch.float32, scale=True),
*adapted_other_args,
**adapted_kwargs,
)
expected = F.to_dtype_image_tensor(info.kernel(input, *other_args, **kwargs), dtype=torch.float32, scale=True)
expected = F.to_dtype_image(info.kernel(input, *other_args, **kwargs), dtype=torch.float32, scale=True)
assert_close(
actual,
......@@ -377,7 +377,7 @@ class TestDispatchers:
if image_datapoint.ndim > 3:
pytest.skip("Input is batched")
image_pil = F.to_image_pil(image_datapoint)
image_pil = F.to_pil_image(image_datapoint)
output = info.dispatcher(image_pil, *other_args, **kwargs)
......@@ -470,7 +470,7 @@ class TestDispatchers:
(F.hflip, F.horizontal_flip),
(F.vflip, F.vertical_flip),
(F.get_image_num_channels, F.get_num_channels),
(F.to_pil_image, F.to_image_pil),
(F.to_pil_image, F.to_pil_image),
(F.elastic_transform, F.elastic),
(F.to_grayscale, F.rgb_to_grayscale),
]
......@@ -493,7 +493,7 @@ def test_normalize_image_tensor_stats(device, num_channels):
mean = image.mean(dim=(1, 2)).tolist()
std = image.std(dim=(1, 2)).tolist()
assert_samples_from_standard_normal(F.normalize_image_tensor(image, mean, std))
assert_samples_from_standard_normal(F.normalize_image(image, mean, std))
class TestClampBoundingBoxes:
......@@ -899,7 +899,7 @@ def test_correctness_center_crop_mask(device, output_size):
_, image_height, image_width = mask.shape
if crop_width > image_height or crop_height > image_width:
padding = _center_crop_compute_padding(crop_height, crop_width, image_height, image_width)
mask = F.pad_image_tensor(mask, padding, fill=0)
mask = F.pad_image(mask, padding, fill=0)
left = round((image_width - crop_width) * 0.5)
top = round((image_height - crop_height) * 0.5)
......@@ -920,7 +920,7 @@ def test_correctness_center_crop_mask(device, output_size):
@pytest.mark.parametrize("ksize", [(3, 3), [3, 5], (23, 23)])
@pytest.mark.parametrize("sigma", [[0.5, 0.5], (0.5, 0.5), (0.8, 0.8), (1.7, 1.7)])
def test_correctness_gaussian_blur_image_tensor(device, canvas_size, dt, ksize, sigma):
fn = F.gaussian_blur_image_tensor
fn = F.gaussian_blur_image
# true_cv2_results = {
# # np_img = np.arange(3 * 10 * 12, dtype="uint8").reshape((10, 12, 3))
......@@ -977,8 +977,8 @@ def test_correctness_gaussian_blur_image_tensor(device, canvas_size, dt, ksize,
PIL.Image.new("RGB", (32, 32), 122),
],
)
def test_to_image_tensor(inpt):
output = F.to_image_tensor(inpt)
def test_to_image(inpt):
output = F.to_image(inpt)
assert isinstance(output, torch.Tensor)
assert output.shape == (3, 32, 32)
......@@ -993,8 +993,8 @@ def test_to_image_tensor(inpt):
],
)
@pytest.mark.parametrize("mode", [None, "RGB"])
def test_to_image_pil(inpt, mode):
output = F.to_image_pil(inpt, mode=mode)
def test_to_pil_image(inpt, mode):
output = F.to_pil_image(inpt, mode=mode)
assert isinstance(output, PIL.Image.Image)
assert np.asarray(inpt).sum() == np.asarray(output).sum()
......@@ -1002,12 +1002,12 @@ def test_to_image_pil(inpt, mode):
def test_equalize_image_tensor_edge_cases():
inpt = torch.zeros(3, 200, 200, dtype=torch.uint8)
output = F.equalize_image_tensor(inpt)
output = F.equalize_image(inpt)
torch.testing.assert_close(inpt, output)
inpt = torch.zeros(5, 3, 200, 200, dtype=torch.uint8)
inpt[..., 100:, 100:] = 1
output = F.equalize_image_tensor(inpt)
output = F.equalize_image(inpt)
assert output.unique().tolist() == [0, 255]
......@@ -1024,7 +1024,7 @@ def test_correctness_uniform_temporal_subsample(device):
# TODO: We can remove this test and related torchvision workaround
# once we fixed related pytorch issue: https://github.com/pytorch/pytorch/issues/68430
@make_info_args_kwargs_parametrization(
[info for info in KERNEL_INFOS if info.kernel is F.resize_image_tensor],
[info for info in KERNEL_INFOS if info.kernel is F.resize_image],
args_kwargs_fn=lambda info: info.reference_inputs_fn(),
)
def test_memory_format_consistency_resize_image_tensor(test_id, info, args_kwargs):
......
test/test_transforms_v2_refactored.py
View file @ ca012d39
......@@ -437,7 +437,7 @@ class TestResize:
check_cuda_vs_cpu_tolerances = dict(rtol=0, atol=atol / 255 if dtype.is_floating_point else atol)
check_kernel(
F.resize_image_tensor,
F.resize_image,
make_image(self.INPUT_SIZE, dtype=dtype, device=device),
size=size,
interpolation=interpolation,
......@@ -495,9 +495,9 @@ class TestResize:
@pytest.mark.parametrize(
("kernel", "input_type"),
[
(F.resize_image_tensor, torch.Tensor),
(F.resize_image_pil, PIL.Image.Image),
(F.resize_image_tensor, datapoints.Image),
(F.resize_image, torch.Tensor),
(F._resize_image_pil, PIL.Image.Image),
(F.resize_image, datapoints.Image),
(F.resize_bounding_boxes, datapoints.BoundingBoxes),
(F.resize_mask, datapoints.Mask),
(F.resize_video, datapoints.Video),
......@@ -541,9 +541,7 @@ class TestResize:
image = make_image(self.INPUT_SIZE, dtype=torch.uint8)
actual = fn(image, size=size, interpolation=interpolation, **max_size_kwarg, antialias=True)
expected = F.to_image_tensor(
F.resize(F.to_image_pil(image), size=size, interpolation=interpolation, **max_size_kwarg)
)
expected = F.to_image(F.resize(F.to_pil_image(image), size=size, interpolation=interpolation, **max_size_kwarg))
self._check_output_size(image, actual, size=size, **max_size_kwarg)
torch.testing.assert_close(actual, expected, atol=1, rtol=0)
......@@ -739,7 +737,7 @@ class TestHorizontalFlip:
@pytest.mark.parametrize("dtype", [torch.float32, torch.uint8])
@pytest.mark.parametrize("device", cpu_and_cuda())
def test_kernel_image_tensor(self, dtype, device):
check_kernel(F.horizontal_flip_image_tensor, make_image(dtype=dtype, device=device))
check_kernel(F.horizontal_flip_image, make_image(dtype=dtype, device=device))
@pytest.mark.parametrize("format", list(datapoints.BoundingBoxFormat))
@pytest.mark.parametrize("dtype", [torch.float32, torch.int64])
......@@ -770,9 +768,9 @@ class TestHorizontalFlip:
@pytest.mark.parametrize(
("kernel", "input_type"),
[
(F.horizontal_flip_image_tensor, torch.Tensor),
(F.horizontal_flip_image_pil, PIL.Image.Image),
(F.horizontal_flip_image_tensor, datapoints.Image),
(F.horizontal_flip_image, torch.Tensor),
(F._horizontal_flip_image_pil, PIL.Image.Image),
(F.horizontal_flip_image, datapoints.Image),
(F.horizontal_flip_bounding_boxes, datapoints.BoundingBoxes),
(F.horizontal_flip_mask, datapoints.Mask),
(F.horizontal_flip_video, datapoints.Video),
......@@ -796,7 +794,7 @@ class TestHorizontalFlip:
image = make_image(dtype=torch.uint8, device="cpu")
actual = fn(image)
expected = F.to_image_tensor(F.horizontal_flip(F.to_image_pil(image)))
expected = F.to_image(F.horizontal_flip(F.to_pil_image(image)))
torch.testing.assert_close(actual, expected)
......@@ -900,7 +898,7 @@ class TestAffine:
if param == "fill":
value = adapt_fill(value, dtype=dtype)
self._check_kernel(
F.affine_image_tensor,
F.affine_image,
make_image(dtype=dtype, device=device),
**{param: value},
check_scripted_vs_eager=not (param in {"shear", "fill"} and isinstance(value, (int, float))),
......@@ -946,9 +944,9 @@ class TestAffine:
@pytest.mark.parametrize(
("kernel", "input_type"),
[
(F.affine_image_tensor, torch.Tensor),
(F.affine_image_pil, PIL.Image.Image),
(F.affine_image_tensor, datapoints.Image),
(F.affine_image, torch.Tensor),
(F._affine_image_pil, PIL.Image.Image),
(F.affine_image, datapoints.Image),
(F.affine_bounding_boxes, datapoints.BoundingBoxes),
(F.affine_mask, datapoints.Mask),
(F.affine_video, datapoints.Video),
......@@ -991,9 +989,9 @@ class TestAffine:
interpolation=interpolation,
fill=fill,
)
expected = F.to_image_tensor(
expected = F.to_image(
F.affine(
F.to_image_pil(image),
F.to_pil_image(image),
angle=angle,
translate=translate,
scale=scale,
......@@ -1026,7 +1024,7 @@ class TestAffine:
actual = transform(image)
torch.manual_seed(seed)
expected = F.to_image_tensor(transform(F.to_image_pil(image)))
expected = F.to_image(transform(F.to_pil_image(image)))
mae = (actual.float() - expected.float()).abs().mean()
assert mae < 2 if interpolation is transforms.InterpolationMode.NEAREST else 8
......@@ -1204,7 +1202,7 @@ class TestVerticalFlip:
@pytest.mark.parametrize("dtype", [torch.float32, torch.uint8])
@pytest.mark.parametrize("device", cpu_and_cuda())
def test_kernel_image_tensor(self, dtype, device):
check_kernel(F.vertical_flip_image_tensor, make_image(dtype=dtype, device=device))
check_kernel(F.vertical_flip_image, make_image(dtype=dtype, device=device))
@pytest.mark.parametrize("format", list(datapoints.BoundingBoxFormat))
@pytest.mark.parametrize("dtype", [torch.float32, torch.int64])
......@@ -1235,9 +1233,9 @@ class TestVerticalFlip:
@pytest.mark.parametrize(
("kernel", "input_type"),
[
(F.vertical_flip_image_tensor, torch.Tensor),
(F.vertical_flip_image_pil, PIL.Image.Image),
(F.vertical_flip_image_tensor, datapoints.Image),
(F.vertical_flip_image, torch.Tensor),
(F._vertical_flip_image_pil, PIL.Image.Image),
(F.vertical_flip_image, datapoints.Image),
(F.vertical_flip_bounding_boxes, datapoints.BoundingBoxes),
(F.vertical_flip_mask, datapoints.Mask),
(F.vertical_flip_video, datapoints.Video),
......@@ -1259,7 +1257,7 @@ class TestVerticalFlip:
image = make_image(dtype=torch.uint8, device="cpu")
actual = fn(image)
expected = F.to_image_tensor(F.vertical_flip(F.to_image_pil(image)))
expected = F.to_image(F.vertical_flip(F.to_pil_image(image)))
torch.testing.assert_close(actual, expected)
......@@ -1339,7 +1337,7 @@ class TestRotate:
if param != "angle":
kwargs["angle"] = self._MINIMAL_AFFINE_KWARGS["angle"]
check_kernel(
F.rotate_image_tensor,
F.rotate_image,
make_image(dtype=dtype, device=device),
**kwargs,
check_scripted_vs_eager=not (param == "fill" and isinstance(value, (int, float))),
......@@ -1385,9 +1383,9 @@ class TestRotate:
@pytest.mark.parametrize(
("kernel", "input_type"),
[
(F.rotate_image_tensor, torch.Tensor),
(F.rotate_image_pil, PIL.Image.Image),
(F.rotate_image_tensor, datapoints.Image),
(F.rotate_image, torch.Tensor),
(F._rotate_image_pil, PIL.Image.Image),
(F.rotate_image, datapoints.Image),
(F.rotate_bounding_boxes, datapoints.BoundingBoxes),
(F.rotate_mask, datapoints.Mask),
(F.rotate_video, datapoints.Video),
......@@ -1419,9 +1417,9 @@ class TestRotate:
fill = adapt_fill(fill, dtype=torch.uint8)
actual = F.rotate(image, angle=angle, center=center, interpolation=interpolation, expand=expand, fill=fill)
expected = F.to_image_tensor(
expected = F.to_image(
F.rotate(
F.to_image_pil(image), angle=angle, center=center, interpolation=interpolation, expand=expand, fill=fill
F.to_pil_image(image), angle=angle, center=center, interpolation=interpolation, expand=expand, fill=fill
)
)
......@@ -1452,7 +1450,7 @@ class TestRotate:
actual = transform(image)
torch.manual_seed(seed)
expected = F.to_image_tensor(transform(F.to_image_pil(image)))
expected = F.to_image(transform(F.to_pil_image(image)))
mae = (actual.float() - expected.float()).abs().mean()
assert mae < 1 if interpolation is transforms.InterpolationMode.NEAREST else 6
......@@ -1621,8 +1619,8 @@ class TestToDtype:
@pytest.mark.parametrize(
("kernel", "make_input"),
[
(F.to_dtype_image_tensor, make_image_tensor),
(F.to_dtype_image_tensor, make_image),
(F.to_dtype_image, make_image_tensor),
(F.to_dtype_image, make_image),
(F.to_dtype_video, make_video),
],
)
......@@ -1801,7 +1799,7 @@ class TestAdjustBrightness:
@pytest.mark.parametrize(
("kernel", "make_input"),
[
(F.adjust_brightness_image_tensor, make_image),
(F.adjust_brightness_image, make_image),
(F.adjust_brightness_video, make_video),
],
)
......@@ -1817,9 +1815,9 @@ class TestAdjustBrightness:
@pytest.mark.parametrize(
("kernel", "input_type"),
[
(F.adjust_brightness_image_tensor, torch.Tensor),
(F.adjust_brightness_image_pil, PIL.Image.Image),
(F.adjust_brightness_image_tensor, datapoints.Image),
(F.adjust_brightness_image, torch.Tensor),
(F._adjust_brightness_image_pil, PIL.Image.Image),
(F.adjust_brightness_image, datapoints.Image),
(F.adjust_brightness_video, datapoints.Video),
],
)
......@@ -1831,7 +1829,7 @@ class TestAdjustBrightness:
image = make_image(dtype=torch.uint8, device="cpu")
actual = F.adjust_brightness(image, brightness_factor=brightness_factor)
expected = F.to_image_tensor(F.adjust_brightness(F.to_image_pil(image), brightness_factor=brightness_factor))
expected = F.to_image(F.adjust_brightness(F.to_pil_image(image), brightness_factor=brightness_factor))
torch.testing.assert_close(actual, expected)
......@@ -1979,9 +1977,9 @@ class TestShapeGetters:
@pytest.mark.parametrize(
("kernel", "make_input"),
[
(F.get_dimensions_image_tensor, make_image_tensor),
(F.get_dimensions_image_pil, make_image_pil),
(F.get_dimensions_image_tensor, make_image),
(F.get_dimensions_image, make_image_tensor),
(F._get_dimensions_image_pil, make_image_pil),
(F.get_dimensions_image, make_image),
(F.get_dimensions_video, make_video),
],
)
......@@ -1996,9 +1994,9 @@ class TestShapeGetters:
@pytest.mark.parametrize(
("kernel", "make_input"),
[
(F.get_num_channels_image_tensor, make_image_tensor),
(F.get_num_channels_image_pil, make_image_pil),
(F.get_num_channels_image_tensor, make_image),
(F.get_num_channels_image, make_image_tensor),
(F._get_num_channels_image_pil, make_image_pil),
(F.get_num_channels_image, make_image),
(F.get_num_channels_video, make_video),
],
)
......@@ -2012,9 +2010,9 @@ class TestShapeGetters:
@pytest.mark.parametrize(
("kernel", "make_input"),
[
(F.get_size_image_tensor, make_image_tensor),
(F.get_size_image_pil, make_image_pil),
(F.get_size_image_tensor, make_image),
(F.get_size_image, make_image_tensor),
(F._get_size_image_pil, make_image_pil),
(F.get_size_image, make_image),
(F.get_size_bounding_boxes, make_bounding_box),
(F.get_size_mask, make_detection_mask),
(F.get_size_mask, make_segmentation_mask),
......@@ -2101,7 +2099,7 @@ class TestRegisterKernel:
F.register_kernel(F.resize, object)
with pytest.raises(ValueError, match="cannot be registered for the builtin datapoint classes"):
F.register_kernel(F.resize, datapoints.Image)(F.resize_image_tensor)
F.register_kernel(F.resize, datapoints.Image)(F.resize_image)
class CustomDatapoint(datapoints.Datapoint):
pass
......@@ -2119,9 +2117,9 @@ class TestGetKernel:
# We are using F.resize as functional and the kernels below as proxy. Any other functional / kernels combination
# would also be fine
KERNELS = {
torch.Tensor: F.resize_image_tensor,
PIL.Image.Image: F.resize_image_pil,
datapoints.Image: F.resize_image_tensor,
torch.Tensor: F.resize_image,
PIL.Image.Image: F._resize_image_pil,
datapoints.Image: F.resize_image,
datapoints.BoundingBoxes: F.resize_bounding_boxes,
datapoints.Mask: F.resize_mask,
datapoints.Video: F.resize_video,
......@@ -2217,10 +2215,10 @@ class TestPermuteChannels:
@pytest.mark.parametrize(
("kernel", "make_input"),
[
(F.permute_channels_image_tensor, make_image_tensor),
(F.permute_channels_image, make_image_tensor),
# FIXME
# check_kernel does not support PIL kernel, but it should
(F.permute_channels_image_tensor, make_image),
(F.permute_channels_image, make_image),
(F.permute_channels_video, make_video),
],
)
......@@ -2236,9 +2234,9 @@ class TestPermuteChannels:
@pytest.mark.parametrize(
("kernel", "input_type"),
[
(F.permute_channels_image_tensor, torch.Tensor),
(F.permute_channels_image_pil, PIL.Image.Image),
(F.permute_channels_image_tensor, datapoints.Image),
(F.permute_channels_image, torch.Tensor),
(F._permute_channels_image_pil, PIL.Image.Image),
(F.permute_channels_image, datapoints.Image),
(F.permute_channels_video, datapoints.Video),
],
)
......
test/test_transforms_v2_utils.py
View file @ ca012d39
......@@ -7,7 +7,7 @@ import torchvision.transforms.v2.utils
from common_utils import DEFAULT_SIZE, make_bounding_box, make_detection_mask, make_image
from torchvision import datapoints
from torchvision.transforms.v2.functional import to_image_pil
from torchvision.transforms.v2.functional import to_pil_image
from torchvision.transforms.v2.utils import has_all, has_any
......@@ -44,7 +44,7 @@ MASK = make_detection_mask(DEFAULT_SIZE)
True,
),
(
(to_image_pil(IMAGE),),
(to_pil_image(IMAGE),),
(datapoints.Image, PIL.Image.Image, torchvision.transforms.v2.utils.is_simple_tensor),
True,
),
......
test/transforms_v2_dispatcher_infos.py
View file @ ca012d39
......@@ -142,32 +142,32 @@ DISPATCHER_INFOS = [
DispatcherInfo(
F.crop,
kernels={
datapoints.Image: F.crop_image_tensor,
datapoints.Image: F.crop_image,
datapoints.Video: F.crop_video,
datapoints.BoundingBoxes: F.crop_bounding_boxes,
datapoints.Mask: F.crop_mask,
},
pil_kernel_info=PILKernelInfo(F.crop_image_pil, kernel_name="crop_image_pil"),
pil_kernel_info=PILKernelInfo(F._crop_image_pil, kernel_name="crop_image_pil"),
),
DispatcherInfo(
F.resized_crop,
kernels={
datapoints.Image: F.resized_crop_image_tensor,
datapoints.Image: F.resized_crop_image,
datapoints.Video: F.resized_crop_video,
datapoints.BoundingBoxes: F.resized_crop_bounding_boxes,
datapoints.Mask: F.resized_crop_mask,
},
pil_kernel_info=PILKernelInfo(F.resized_crop_image_pil),
pil_kernel_info=PILKernelInfo(F._resized_crop_image_pil),
),
DispatcherInfo(
F.pad,
kernels={
datapoints.Image: F.pad_image_tensor,
datapoints.Image: F.pad_image,
datapoints.Video: F.pad_video,
datapoints.BoundingBoxes: F.pad_bounding_boxes,
datapoints.Mask: F.pad_mask,
},
pil_kernel_info=PILKernelInfo(F.pad_image_pil, kernel_name="pad_image_pil"),
pil_kernel_info=PILKernelInfo(F._pad_image_pil, kernel_name="pad_image_pil"),
test_marks=[
*xfails_pil(
reason=(
......@@ -184,12 +184,12 @@ DISPATCHER_INFOS = [
DispatcherInfo(
F.perspective,
kernels={
datapoints.Image: F.perspective_image_tensor,
datapoints.Image: F.perspective_image,
datapoints.Video: F.perspective_video,
datapoints.BoundingBoxes: F.perspective_bounding_boxes,
datapoints.Mask: F.perspective_mask,
},
pil_kernel_info=PILKernelInfo(F.perspective_image_pil),
pil_kernel_info=PILKernelInfo(F._perspective_image_pil),
test_marks=[
*xfails_pil_if_fill_sequence_needs_broadcast,
xfail_jit_python_scalar_arg("fill"),
......@@ -198,23 +198,23 @@ DISPATCHER_INFOS = [
DispatcherInfo(
F.elastic,
kernels={
datapoints.Image: F.elastic_image_tensor,
datapoints.Image: F.elastic_image,
datapoints.Video: F.elastic_video,
datapoints.BoundingBoxes: F.elastic_bounding_boxes,
datapoints.Mask: F.elastic_mask,
},
pil_kernel_info=PILKernelInfo(F.elastic_image_pil),
pil_kernel_info=PILKernelInfo(F._elastic_image_pil),
test_marks=[xfail_jit_python_scalar_arg("fill")],
),
DispatcherInfo(
F.center_crop,
kernels={
datapoints.Image: F.center_crop_image_tensor,
datapoints.Image: F.center_crop_image,
datapoints.Video: F.center_crop_video,
datapoints.BoundingBoxes: F.center_crop_bounding_boxes,
datapoints.Mask: F.center_crop_mask,
},
pil_kernel_info=PILKernelInfo(F.center_crop_image_pil),
pil_kernel_info=PILKernelInfo(F._center_crop_image_pil),
test_marks=[
xfail_jit_python_scalar_arg("output_size"),
],
......@@ -222,10 +222,10 @@ DISPATCHER_INFOS = [
DispatcherInfo(
F.gaussian_blur,
kernels={
datapoints.Image: F.gaussian_blur_image_tensor,
datapoints.Image: F.gaussian_blur_image,
datapoints.Video: F.gaussian_blur_video,
},
pil_kernel_info=PILKernelInfo(F.gaussian_blur_image_pil),
pil_kernel_info=PILKernelInfo(F._gaussian_blur_image_pil),
test_marks=[
xfail_jit_python_scalar_arg("kernel_size"),
xfail_jit_python_scalar_arg("sigma"),
......@@ -234,58 +234,58 @@ DISPATCHER_INFOS = [
DispatcherInfo(
F.equalize,
kernels={
datapoints.Image: F.equalize_image_tensor,
datapoints.Image: F.equalize_image,
datapoints.Video: F.equalize_video,
},
pil_kernel_info=PILKernelInfo(F.equalize_image_pil, kernel_name="equalize_image_pil"),
pil_kernel_info=PILKernelInfo(F._equalize_image_pil, kernel_name="equalize_image_pil"),
),
DispatcherInfo(
F.invert,
kernels={
datapoints.Image: F.invert_image_tensor,
datapoints.Image: F.invert_image,
datapoints.Video: F.invert_video,
},
pil_kernel_info=PILKernelInfo(F.invert_image_pil, kernel_name="invert_image_pil"),
pil_kernel_info=PILKernelInfo(F._invert_image_pil, kernel_name="invert_image_pil"),
),
DispatcherInfo(
F.posterize,
kernels={
datapoints.Image: F.posterize_image_tensor,
datapoints.Image: F.posterize_image,
datapoints.Video: F.posterize_video,
},
pil_kernel_info=PILKernelInfo(F.posterize_image_pil, kernel_name="posterize_image_pil"),
pil_kernel_info=PILKernelInfo(F._posterize_image_pil, kernel_name="posterize_image_pil"),
),
DispatcherInfo(
F.solarize,
kernels={
datapoints.Image: F.solarize_image_tensor,
datapoints.Image: F.solarize_image,
datapoints.Video: F.solarize_video,
},
pil_kernel_info=PILKernelInfo(F.solarize_image_pil, kernel_name="solarize_image_pil"),
pil_kernel_info=PILKernelInfo(F._solarize_image_pil, kernel_name="solarize_image_pil"),
),
DispatcherInfo(
F.autocontrast,
kernels={
datapoints.Image: F.autocontrast_image_tensor,
datapoints.Image: F.autocontrast_image,
datapoints.Video: F.autocontrast_video,
},
pil_kernel_info=PILKernelInfo(F.autocontrast_image_pil, kernel_name="autocontrast_image_pil"),
pil_kernel_info=PILKernelInfo(F._autocontrast_image_pil, kernel_name="autocontrast_image_pil"),
),
DispatcherInfo(
F.adjust_sharpness,
kernels={
datapoints.Image: F.adjust_sharpness_image_tensor,
datapoints.Image: F.adjust_sharpness_image,
datapoints.Video: F.adjust_sharpness_video,
},
pil_kernel_info=PILKernelInfo(F.adjust_sharpness_image_pil, kernel_name="adjust_sharpness_image_pil"),
pil_kernel_info=PILKernelInfo(F._adjust_sharpness_image_pil, kernel_name="adjust_sharpness_image_pil"),
),
DispatcherInfo(
F.erase,
kernels={
datapoints.Image: F.erase_image_tensor,
datapoints.Image: F.erase_image,
datapoints.Video: F.erase_video,
},
pil_kernel_info=PILKernelInfo(F.erase_image_pil),
pil_kernel_info=PILKernelInfo(F._erase_image_pil),
test_marks=[
skip_dispatch_datapoint,
],
......@@ -293,42 +293,42 @@ DISPATCHER_INFOS = [
DispatcherInfo(
F.adjust_contrast,
kernels={
datapoints.Image: F.adjust_contrast_image_tensor,
datapoints.Image: F.adjust_contrast_image,
datapoints.Video: F.adjust_contrast_video,
},
pil_kernel_info=PILKernelInfo(F.adjust_contrast_image_pil, kernel_name="adjust_contrast_image_pil"),
pil_kernel_info=PILKernelInfo(F._adjust_contrast_image_pil, kernel_name="adjust_contrast_image_pil"),
),
DispatcherInfo(
F.adjust_gamma,
kernels={
datapoints.Image: F.adjust_gamma_image_tensor,
datapoints.Image: F.adjust_gamma_image,
datapoints.Video: F.adjust_gamma_video,
},
pil_kernel_info=PILKernelInfo(F.adjust_gamma_image_pil, kernel_name="adjust_gamma_image_pil"),
pil_kernel_info=PILKernelInfo(F._adjust_gamma_image_pil, kernel_name="adjust_gamma_image_pil"),
),
DispatcherInfo(
F.adjust_hue,
kernels={
datapoints.Image: F.adjust_hue_image_tensor,
datapoints.Image: F.adjust_hue_image,
datapoints.Video: F.adjust_hue_video,
},
pil_kernel_info=PILKernelInfo(F.adjust_hue_image_pil, kernel_name="adjust_hue_image_pil"),
pil_kernel_info=PILKernelInfo(F._adjust_hue_image_pil, kernel_name="adjust_hue_image_pil"),
),
DispatcherInfo(
F.adjust_saturation,
kernels={
datapoints.Image: F.adjust_saturation_image_tensor,
datapoints.Image: F.adjust_saturation_image,
datapoints.Video: F.adjust_saturation_video,
},
pil_kernel_info=PILKernelInfo(F.adjust_saturation_image_pil, kernel_name="adjust_saturation_image_pil"),
pil_kernel_info=PILKernelInfo(F._adjust_saturation_image_pil, kernel_name="adjust_saturation_image_pil"),
),
DispatcherInfo(
F.five_crop,
kernels={
datapoints.Image: F.five_crop_image_tensor,
datapoints.Image: F.five_crop_image,
datapoints.Video: F.five_crop_video,
},
pil_kernel_info=PILKernelInfo(F.five_crop_image_pil),
pil_kernel_info=PILKernelInfo(F._five_crop_image_pil),
test_marks=[
xfail_jit_python_scalar_arg("size"),
*multi_crop_skips,
......@@ -337,19 +337,19 @@ DISPATCHER_INFOS = [
DispatcherInfo(
F.ten_crop,
kernels={
datapoints.Image: F.ten_crop_image_tensor,
datapoints.Image: F.ten_crop_image,
datapoints.Video: F.ten_crop_video,
},
test_marks=[
xfail_jit_python_scalar_arg("size"),
*multi_crop_skips,
],
pil_kernel_info=PILKernelInfo(F.ten_crop_image_pil),
pil_kernel_info=PILKernelInfo(F._ten_crop_image_pil),
),
DispatcherInfo(
F.normalize,
kernels={
datapoints.Image: F.normalize_image_tensor,
datapoints.Image: F.normalize_image,
datapoints.Video: F.normalize_video,
},
test_marks=[
......
test/transforms_v2_kernel_infos.py
View file @ ca012d39
......@@ -122,12 +122,12 @@ def pil_reference_wrapper(pil_kernel):
f"Can only test single tensor images against PIL, but input has shape {input_tensor.shape}"
)
input_pil = F.to_image_pil(input_tensor)
input_pil = F.to_pil_image(input_tensor)
output_pil = pil_kernel(input_pil, *other_args, **kwargs)
if not isinstance(output_pil, PIL.Image.Image):
return output_pil
output_tensor = F.to_image_tensor(output_pil)
output_tensor = F.to_image(output_pil)
# 2D mask shenanigans
if output_tensor.ndim == 2 and input_tensor.ndim == 3:
......@@ -331,10 +331,10 @@ def reference_inputs_crop_bounding_boxes():
KERNEL_INFOS.extend(
[
KernelInfo(
F.crop_image_tensor,
F.crop_image,
kernel_name="crop_image_tensor",
sample_inputs_fn=sample_inputs_crop_image_tensor,
reference_fn=pil_reference_wrapper(F.crop_image_pil),
reference_fn=pil_reference_wrapper(F._crop_image_pil),
reference_inputs_fn=reference_inputs_crop_image_tensor,
float32_vs_uint8=True,
),
......@@ -347,7 +347,7 @@ KERNEL_INFOS.extend(
KernelInfo(
F.crop_mask,
sample_inputs_fn=sample_inputs_crop_mask,
reference_fn=pil_reference_wrapper(F.crop_image_pil),
reference_fn=pil_reference_wrapper(F._crop_image_pil),
reference_inputs_fn=reference_inputs_crop_mask,
float32_vs_uint8=True,
),
......@@ -373,7 +373,7 @@ def reference_resized_crop_image_tensor(*args, **kwargs):
F.InterpolationMode.BICUBIC,
}:
raise pytest.UsageError("Anti-aliasing is always active in PIL")
return F.resized_crop_image_pil(*args, **kwargs)
return F._resized_crop_image_pil(*args, **kwargs)
def reference_inputs_resized_crop_image_tensor():
......@@ -417,7 +417,7 @@ def sample_inputs_resized_crop_video():
KERNEL_INFOS.extend(
[
KernelInfo(
F.resized_crop_image_tensor,
F.resized_crop_image,
sample_inputs_fn=sample_inputs_resized_crop_image_tensor,
reference_fn=reference_resized_crop_image_tensor,
reference_inputs_fn=reference_inputs_resized_crop_image_tensor,
......@@ -570,9 +570,9 @@ def pad_xfail_jit_fill_condition(args_kwargs):
KERNEL_INFOS.extend(
[
KernelInfo(
F.pad_image_tensor,
F.pad_image,
sample_inputs_fn=sample_inputs_pad_image_tensor,
reference_fn=pil_reference_wrapper(F.pad_image_pil),
reference_fn=pil_reference_wrapper(F._pad_image_pil),
reference_inputs_fn=reference_inputs_pad_image_tensor,
float32_vs_uint8=float32_vs_uint8_fill_adapter,
closeness_kwargs=float32_vs_uint8_pixel_difference(),
......@@ -595,7 +595,7 @@ KERNEL_INFOS.extend(
KernelInfo(
F.pad_mask,
sample_inputs_fn=sample_inputs_pad_mask,
reference_fn=pil_reference_wrapper(F.pad_image_pil),
reference_fn=pil_reference_wrapper(F._pad_image_pil),
reference_inputs_fn=reference_inputs_pad_mask,
float32_vs_uint8=float32_vs_uint8_fill_adapter,
),
......@@ -690,9 +690,9 @@ def sample_inputs_perspective_video():
KERNEL_INFOS.extend(
[
KernelInfo(
F.perspective_image_tensor,
F.perspective_image,
sample_inputs_fn=sample_inputs_perspective_image_tensor,
reference_fn=pil_reference_wrapper(F.perspective_image_pil),
reference_fn=pil_reference_wrapper(F._perspective_image_pil),
reference_inputs_fn=reference_inputs_perspective_image_tensor,
float32_vs_uint8=float32_vs_uint8_fill_adapter,
closeness_kwargs={
......@@ -715,7 +715,7 @@ KERNEL_INFOS.extend(
KernelInfo(
F.perspective_mask,
sample_inputs_fn=sample_inputs_perspective_mask,
reference_fn=pil_reference_wrapper(F.perspective_image_pil),
reference_fn=pil_reference_wrapper(F._perspective_image_pil),
reference_inputs_fn=reference_inputs_perspective_mask,
float32_vs_uint8=True,
closeness_kwargs={
......@@ -786,7 +786,7 @@ def sample_inputs_elastic_video():
KERNEL_INFOS.extend(
[
KernelInfo(
F.elastic_image_tensor,
F.elastic_image,
sample_inputs_fn=sample_inputs_elastic_image_tensor,
reference_inputs_fn=reference_inputs_elastic_image_tensor,
float32_vs_uint8=float32_vs_uint8_fill_adapter,
......@@ -870,9 +870,9 @@ def sample_inputs_center_crop_video():
KERNEL_INFOS.extend(
[
KernelInfo(
F.center_crop_image_tensor,
F.center_crop_image,
sample_inputs_fn=sample_inputs_center_crop_image_tensor,
reference_fn=pil_reference_wrapper(F.center_crop_image_pil),
reference_fn=pil_reference_wrapper(F._center_crop_image_pil),
reference_inputs_fn=reference_inputs_center_crop_image_tensor,
float32_vs_uint8=True,
test_marks=[
......@@ -889,7 +889,7 @@ KERNEL_INFOS.extend(
KernelInfo(
F.center_crop_mask,
sample_inputs_fn=sample_inputs_center_crop_mask,
reference_fn=pil_reference_wrapper(F.center_crop_image_pil),
reference_fn=pil_reference_wrapper(F._center_crop_image_pil),
reference_inputs_fn=reference_inputs_center_crop_mask,
float32_vs_uint8=True,
test_marks=[
......@@ -924,7 +924,7 @@ def sample_inputs_gaussian_blur_video():
KERNEL_INFOS.extend(
[
KernelInfo(
F.gaussian_blur_image_tensor,
F.gaussian_blur_image,
sample_inputs_fn=sample_inputs_gaussian_blur_image_tensor,
closeness_kwargs=cuda_vs_cpu_pixel_difference(),
test_marks=[
......@@ -1010,10 +1010,10 @@ def sample_inputs_equalize_video():
KERNEL_INFOS.extend(
[
KernelInfo(
F.equalize_image_tensor,
F.equalize_image,
kernel_name="equalize_image_tensor",
sample_inputs_fn=sample_inputs_equalize_image_tensor,
reference_fn=pil_reference_wrapper(F.equalize_image_pil),
reference_fn=pil_reference_wrapper(F._equalize_image_pil),
float32_vs_uint8=True,
reference_inputs_fn=reference_inputs_equalize_image_tensor,
),
......@@ -1043,10 +1043,10 @@ def sample_inputs_invert_video():
KERNEL_INFOS.extend(
[
KernelInfo(
F.invert_image_tensor,
F.invert_image,
kernel_name="invert_image_tensor",
sample_inputs_fn=sample_inputs_invert_image_tensor,
reference_fn=pil_reference_wrapper(F.invert_image_pil),
reference_fn=pil_reference_wrapper(F._invert_image_pil),
reference_inputs_fn=reference_inputs_invert_image_tensor,
float32_vs_uint8=True,
),
......@@ -1082,10 +1082,10 @@ def sample_inputs_posterize_video():
KERNEL_INFOS.extend(
[
KernelInfo(
F.posterize_image_tensor,
F.posterize_image,
kernel_name="posterize_image_tensor",
sample_inputs_fn=sample_inputs_posterize_image_tensor,
reference_fn=pil_reference_wrapper(F.posterize_image_pil),
reference_fn=pil_reference_wrapper(F._posterize_image_pil),
reference_inputs_fn=reference_inputs_posterize_image_tensor,
float32_vs_uint8=True,
closeness_kwargs=float32_vs_uint8_pixel_difference(),
......@@ -1127,10 +1127,10 @@ def sample_inputs_solarize_video():
KERNEL_INFOS.extend(
[
KernelInfo(
F.solarize_image_tensor,
F.solarize_image,
kernel_name="solarize_image_tensor",
sample_inputs_fn=sample_inputs_solarize_image_tensor,
reference_fn=pil_reference_wrapper(F.solarize_image_pil),
reference_fn=pil_reference_wrapper(F._solarize_image_pil),
reference_inputs_fn=reference_inputs_solarize_image_tensor,
float32_vs_uint8=uint8_to_float32_threshold_adapter,
closeness_kwargs=float32_vs_uint8_pixel_difference(),
......@@ -1161,10 +1161,10 @@ def sample_inputs_autocontrast_video():
KERNEL_INFOS.extend(
[
KernelInfo(
F.autocontrast_image_tensor,
F.autocontrast_image,
kernel_name="autocontrast_image_tensor",
sample_inputs_fn=sample_inputs_autocontrast_image_tensor,
reference_fn=pil_reference_wrapper(F.autocontrast_image_pil),
reference_fn=pil_reference_wrapper(F._autocontrast_image_pil),
reference_inputs_fn=reference_inputs_autocontrast_image_tensor,
float32_vs_uint8=True,
closeness_kwargs={
......@@ -1206,10 +1206,10 @@ def sample_inputs_adjust_sharpness_video():
KERNEL_INFOS.extend(
[
KernelInfo(
F.adjust_sharpness_image_tensor,
F.adjust_sharpness_image,
kernel_name="adjust_sharpness_image_tensor",
sample_inputs_fn=sample_inputs_adjust_sharpness_image_tensor,
reference_fn=pil_reference_wrapper(F.adjust_sharpness_image_pil),
reference_fn=pil_reference_wrapper(F._adjust_sharpness_image_pil),
reference_inputs_fn=reference_inputs_adjust_sharpness_image_tensor,
float32_vs_uint8=True,
closeness_kwargs=float32_vs_uint8_pixel_difference(2),
......@@ -1241,7 +1241,7 @@ def sample_inputs_erase_video():
KERNEL_INFOS.extend(
[
KernelInfo(
F.erase_image_tensor,
F.erase_image,
kernel_name="erase_image_tensor",
sample_inputs_fn=sample_inputs_erase_image_tensor,
),
......@@ -1276,10 +1276,10 @@ def sample_inputs_adjust_contrast_video():
KERNEL_INFOS.extend(
[
KernelInfo(
F.adjust_contrast_image_tensor,
F.adjust_contrast_image,
kernel_name="adjust_contrast_image_tensor",
sample_inputs_fn=sample_inputs_adjust_contrast_image_tensor,
reference_fn=pil_reference_wrapper(F.adjust_contrast_image_pil),
reference_fn=pil_reference_wrapper(F._adjust_contrast_image_pil),
reference_inputs_fn=reference_inputs_adjust_contrast_image_tensor,
float32_vs_uint8=True,
closeness_kwargs={
......@@ -1329,10 +1329,10 @@ def sample_inputs_adjust_gamma_video():
KERNEL_INFOS.extend(
[
KernelInfo(
F.adjust_gamma_image_tensor,
F.adjust_gamma_image,
kernel_name="adjust_gamma_image_tensor",
sample_inputs_fn=sample_inputs_adjust_gamma_image_tensor,
reference_fn=pil_reference_wrapper(F.adjust_gamma_image_pil),
reference_fn=pil_reference_wrapper(F._adjust_gamma_image_pil),
reference_inputs_fn=reference_inputs_adjust_gamma_image_tensor,
float32_vs_uint8=True,
closeness_kwargs={
......@@ -1372,10 +1372,10 @@ def sample_inputs_adjust_hue_video():
KERNEL_INFOS.extend(
[
KernelInfo(
F.adjust_hue_image_tensor,
F.adjust_hue_image,
kernel_name="adjust_hue_image_tensor",
sample_inputs_fn=sample_inputs_adjust_hue_image_tensor,
reference_fn=pil_reference_wrapper(F.adjust_hue_image_pil),
reference_fn=pil_reference_wrapper(F._adjust_hue_image_pil),
reference_inputs_fn=reference_inputs_adjust_hue_image_tensor,
float32_vs_uint8=True,
closeness_kwargs={
......@@ -1414,10 +1414,10 @@ def sample_inputs_adjust_saturation_video():
KERNEL_INFOS.extend(
[
KernelInfo(
F.adjust_saturation_image_tensor,
F.adjust_saturation_image,
kernel_name="adjust_saturation_image_tensor",
sample_inputs_fn=sample_inputs_adjust_saturation_image_tensor,
reference_fn=pil_reference_wrapper(F.adjust_saturation_image_pil),
reference_fn=pil_reference_wrapper(F._adjust_saturation_image_pil),
reference_inputs_fn=reference_inputs_adjust_saturation_image_tensor,
float32_vs_uint8=True,
closeness_kwargs={
......@@ -1517,8 +1517,7 @@ def multi_crop_pil_reference_wrapper(pil_kernel):
def wrapper(input_tensor, *other_args, **kwargs):
output = pil_reference_wrapper(pil_kernel)(input_tensor, *other_args, **kwargs)
return type(output)(
F.to_dtype_image_tensor(F.to_image_tensor(output_pil), dtype=input_tensor.dtype, scale=True)
for output_pil in output
F.to_dtype_image(F.to_image(output_pil), dtype=input_tensor.dtype, scale=True) for output_pil in output
)
return wrapper
......@@ -1532,9 +1531,9 @@ _common_five_ten_crop_marks = [
KERNEL_INFOS.extend(
[
KernelInfo(
F.five_crop_image_tensor,
F.five_crop_image,
sample_inputs_fn=sample_inputs_five_crop_image_tensor,
reference_fn=multi_crop_pil_reference_wrapper(F.five_crop_image_pil),
reference_fn=multi_crop_pil_reference_wrapper(F._five_crop_image_pil),
reference_inputs_fn=reference_inputs_five_crop_image_tensor,
test_marks=_common_five_ten_crop_marks,
),
......@@ -1544,9 +1543,9 @@ KERNEL_INFOS.extend(
test_marks=_common_five_ten_crop_marks,
),
KernelInfo(
F.ten_crop_image_tensor,
F.ten_crop_image,
sample_inputs_fn=sample_inputs_ten_crop_image_tensor,
reference_fn=multi_crop_pil_reference_wrapper(F.ten_crop_image_pil),
reference_fn=multi_crop_pil_reference_wrapper(F._ten_crop_image_pil),
reference_inputs_fn=reference_inputs_ten_crop_image_tensor,
test_marks=_common_five_ten_crop_marks,
),
......@@ -1600,7 +1599,7 @@ def sample_inputs_normalize_video():
KERNEL_INFOS.extend(
[
KernelInfo(
F.normalize_image_tensor,
F.normalize_image,
kernel_name="normalize_image_tensor",
sample_inputs_fn=sample_inputs_normalize_image_tensor,
reference_fn=reference_normalize_image_tensor,
......
torchvision/prototype/transforms/_augment.py
View file @ ca012d39
......@@ -112,7 +112,7 @@ class SimpleCopyPaste(Transform):
if isinstance(obj, datapoints.Image) or is_simple_tensor(obj):
images.append(obj)
elif isinstance(obj, PIL.Image.Image):
images.append(F.to_image_tensor(obj))
images.append(F.to_image(obj))
elif isinstance(obj, datapoints.BoundingBoxes):
bboxes.append(obj)
elif isinstance(obj, datapoints.Mask):
......@@ -144,7 +144,7 @@ class SimpleCopyPaste(Transform):
flat_sample[i] = datapoints.wrap(output_images[c0], like=obj)
c0 += 1
elif isinstance(obj, PIL.Image.Image):
flat_sample[i] = F.to_image_pil(output_images[c0])
flat_sample[i] = F.to_pil_image(output_images[c0])
c0 += 1
elif is_simple_tensor(obj):
flat_sample[i] = output_images[c0]
......
torchvision/transforms/v2/__init__.py
View file @ ca012d39
......@@ -52,7 +52,7 @@ from ._misc import (
ToDtype,
)
from ._temporal import UniformTemporalSubsample
from ._type_conversion import PILToTensor, ToImagePIL, ToImageTensor, ToPILImage
from ._type_conversion import PILToTensor, ToImage, ToPILImage
from ._deprecated import ToTensor # usort: skip
......
torchvision/transforms/v2/_auto_augment.py
View file @ ca012d39
......@@ -622,6 +622,6 @@ class AugMix(_AutoAugmentBase):
if isinstance(orig_image_or_video, (datapoints.Image, datapoints.Video)):
mix = datapoints.wrap(mix, like=orig_image_or_video)
elif isinstance(orig_image_or_video, PIL.Image.Image):
mix = F.to_image_pil(mix)
mix = F.to_pil_image(mix)
return self._unflatten_and_insert_image_or_video(flat_inputs_with_spec, mix)
torchvision/transforms/v2/_type_conversion.py
View file @ ca012d39
......@@ -26,7 +26,7 @@ class PILToTensor(Transform):
return F.pil_to_tensor(inpt)
class ToImageTensor(Transform):
class ToImage(Transform):
"""[BETA] Convert a tensor, ndarray, or PIL Image to :class:`~torchvision.datapoints.Image`
; this does not scale values.
......@@ -40,10 +40,10 @@ class ToImageTensor(Transform):
def _transform(
self, inpt: Union[torch.Tensor, PIL.Image.Image, np.ndarray], params: Dict[str, Any]
) -> datapoints.Image:
return F.to_image_tensor(inpt)
return F.to_image(inpt)
class ToImagePIL(Transform):
class ToPILImage(Transform):
"""[BETA] Convert a tensor or an ndarray to PIL Image - this does not scale values.
.. v2betastatus:: ToImagePIL transform
......@@ -74,9 +74,4 @@ class ToImagePIL(Transform):
def _transform(
self, inpt: Union[torch.Tensor, PIL.Image.Image, np.ndarray], params: Dict[str, Any]
) -> PIL.Image.Image:
return F.to_image_pil(inpt, mode=self.mode)
# We changed the name to align them with the new naming scheme. Still, `ToPILImage` is
# prevalent and well understood. Thus, we just alias it without deprecating the old name.
ToPILImage = ToImagePIL
return F.to_pil_image(inpt, mode=self.mode)
torchvision/transforms/v2/functional/__init__.py
View file @ ca012d39
......@@ -5,173 +5,173 @@ from ._utils import is_simple_tensor, register_kernel # usort: skip
from ._meta import (
clamp_bounding_boxes,
convert_format_bounding_boxes,
get_dimensions_image_tensor,
get_dimensions_image_pil,
get_dimensions_image,
_get_dimensions_image_pil,
get_dimensions_video,
get_dimensions,
get_num_frames_video,
get_num_frames,
get_image_num_channels,
get_num_channels_image_tensor,
get_num_channels_image_pil,
get_num_channels_image,
_get_num_channels_image_pil,
get_num_channels_video,
get_num_channels,
get_size_bounding_boxes,
get_size_image_tensor,
get_size_image_pil,
get_size_image,
_get_size_image_pil,
get_size_mask,
get_size_video,
get_size,
) # usort: skip
from ._augment import erase, erase_image_pil, erase_image_tensor, erase_video
from ._augment import _erase_image_pil, erase, erase_image, erase_video
from ._color import (
_adjust_brightness_image_pil,
_adjust_contrast_image_pil,
_adjust_gamma_image_pil,
_adjust_hue_image_pil,
_adjust_saturation_image_pil,
_adjust_sharpness_image_pil,
_autocontrast_image_pil,
_equalize_image_pil,
_invert_image_pil,
_permute_channels_image_pil,
_posterize_image_pil,
_rgb_to_grayscale_image_pil,
_solarize_image_pil,
adjust_brightness,
adjust_brightness_image_pil,
adjust_brightness_image_tensor,
adjust_brightness_image,
adjust_brightness_video,
adjust_contrast,
adjust_contrast_image_pil,
adjust_contrast_image_tensor,
adjust_contrast_image,
adjust_contrast_video,
adjust_gamma,
adjust_gamma_image_pil,
adjust_gamma_image_tensor,
adjust_gamma_image,
adjust_gamma_video,
adjust_hue,
adjust_hue_image_pil,
adjust_hue_image_tensor,
adjust_hue_image,
adjust_hue_video,
adjust_saturation,
adjust_saturation_image_pil,
adjust_saturation_image_tensor,
adjust_saturation_image,
adjust_saturation_video,
adjust_sharpness,
adjust_sharpness_image_pil,
adjust_sharpness_image_tensor,
adjust_sharpness_image,
adjust_sharpness_video,
autocontrast,
autocontrast_image_pil,
autocontrast_image_tensor,
autocontrast_image,
autocontrast_video,
equalize,
equalize_image_pil,
equalize_image_tensor,
equalize_image,
equalize_video,
invert,
invert_image_pil,
invert_image_tensor,
invert_image,
invert_video,
permute_channels,
permute_channels_image_pil,
permute_channels_image_tensor,
permute_channels_image,
permute_channels_video,
posterize,
posterize_image_pil,
posterize_image_tensor,
posterize_image,
posterize_video,
rgb_to_grayscale,
rgb_to_grayscale_image_pil,
rgb_to_grayscale_image_tensor,
rgb_to_grayscale_image,
solarize,
solarize_image_pil,
solarize_image_tensor,
solarize_image,
solarize_video,
to_grayscale,
)
from ._geometry import (
_affine_image_pil,
_center_crop_image_pil,
_crop_image_pil,
_elastic_image_pil,
_five_crop_image_pil,
_horizontal_flip_image_pil,
_pad_image_pil,
_perspective_image_pil,
_resize_image_pil,
_resized_crop_image_pil,
_rotate_image_pil,
_ten_crop_image_pil,
_vertical_flip_image_pil,
affine,
affine_bounding_boxes,
affine_image_pil,
affine_image_tensor,
affine_image,
affine_mask,
affine_video,
center_crop,
center_crop_bounding_boxes,
center_crop_image_pil,
center_crop_image_tensor,
center_crop_image,
center_crop_mask,
center_crop_video,
crop,
crop_bounding_boxes,
crop_image_pil,
crop_image_tensor,
crop_image,
crop_mask,
crop_video,
elastic,
elastic_bounding_boxes,
elastic_image_pil,
elastic_image_tensor,
elastic_image,
elastic_mask,
elastic_transform,
elastic_video,
five_crop,
five_crop_image_pil,
five_crop_image_tensor,
five_crop_image,
five_crop_video,
hflip, # TODO: Consider moving all pure alias definitions at the bottom of the file
horizontal_flip,
horizontal_flip_bounding_boxes,
horizontal_flip_image_pil,
horizontal_flip_image_tensor,
horizontal_flip_image,
horizontal_flip_mask,
horizontal_flip_video,
pad,
pad_bounding_boxes,
pad_image_pil,
pad_image_tensor,
pad_image,
pad_mask,
pad_video,
perspective,
perspective_bounding_boxes,
perspective_image_pil,
perspective_image_tensor,
perspective_image,
perspective_mask,
perspective_video,
resize,
resize_bounding_boxes,
resize_image_pil,
resize_image_tensor,
resize_image,
resize_mask,
resize_video,
resized_crop,
resized_crop_bounding_boxes,
resized_crop_image_pil,
resized_crop_image_tensor,
resized_crop_image,
resized_crop_mask,
resized_crop_video,
rotate,
rotate_bounding_boxes,
rotate_image_pil,
rotate_image_tensor,
rotate_image,
rotate_mask,
rotate_video,
ten_crop,
ten_crop_image_pil,
ten_crop_image_tensor,
ten_crop_image,
ten_crop_video,
vertical_flip,
vertical_flip_bounding_boxes,
vertical_flip_image_pil,
vertical_flip_image_tensor,
vertical_flip_image,
vertical_flip_mask,
vertical_flip_video,
vflip,
)
from ._misc import (
_gaussian_blur_image_pil,
convert_image_dtype,
gaussian_blur,
gaussian_blur_image_pil,
gaussian_blur_image_tensor,
gaussian_blur_image,
gaussian_blur_video,
normalize,
normalize_image_tensor,
normalize_image,
normalize_video,
to_dtype,
to_dtype_image_tensor,
to_dtype_image,
to_dtype_video,
)
from ._temporal import uniform_temporal_subsample, uniform_temporal_subsample_video
from ._type_conversion import pil_to_tensor, to_image_pil, to_image_tensor, to_pil_image
from ._type_conversion import pil_to_tensor, to_image, to_pil_image
from ._deprecated import get_image_size, to_tensor # usort: skip
torchvision/transforms/v2/functional/_augment.py
View file @ ca012d39
......@@ -18,7 +18,7 @@ def erase(
inplace: bool = False,
) -> torch.Tensor:
if torch.jit.is_scripting():
return erase_image_tensor(inpt, i=i, j=j, h=h, w=w, v=v, inplace=inplace)
return erase_image(inpt, i=i, j=j, h=h, w=w, v=v, inplace=inplace)
_log_api_usage_once(erase)
......@@ -28,7 +28,7 @@ def erase(
@_register_kernel_internal(erase, torch.Tensor)
@_register_kernel_internal(erase, datapoints.Image)
def erase_image_tensor(
def erase_image(
image: torch.Tensor, i: int, j: int, h: int, w: int, v: torch.Tensor, inplace: bool = False
) -> torch.Tensor:
if not inplace:
......@@ -39,11 +39,11 @@ def erase_image_tensor(
@_register_kernel_internal(erase, PIL.Image.Image)
def erase_image_pil(
def _erase_image_pil(
image: PIL.Image.Image, i: int, j: int, h: int, w: int, v: torch.Tensor, inplace: bool = False
) -> PIL.Image.Image:
t_img = pil_to_tensor(image)
output = erase_image_tensor(t_img, i=i, j=j, h=h, w=w, v=v, inplace=inplace)
output = erase_image(t_img, i=i, j=j, h=h, w=w, v=v, inplace=inplace)
return to_pil_image(output, mode=image.mode)
......@@ -51,4 +51,4 @@ def erase_image_pil(
def erase_video(
video: torch.Tensor, i: int, j: int, h: int, w: int, v: torch.Tensor, inplace: bool = False
) -> torch.Tensor:
return erase_image_tensor(video, i=i, j=j, h=h, w=w, v=v, inplace=inplace)
return erase_image(video, i=i, j=j, h=h, w=w, v=v, inplace=inplace)
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