Promote prototype transforms to beta status (#7261)

Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>
Co-authored-by: vfdev-5 <vfdev.5@gmail.com>

test/datasets_utils.py

@@ -584,11 +584,8 @@ class DatasetTestCase(unittest.TestCase):
 
     @test_all_configs
     def test_transforms_v2_wrapper(self, config):
-        # Although this is a stable test, we unconditionally import from `torchvision.prototype` here. The wrapper needs
-        # to be available with the next release when v2 is released. Thus, if this import somehow fails on the release
-        # branch, we screwed up the roll-out
-        from torchvision.prototype.datapoints import wrap_dataset_for_transforms_v2
-        from torchvision.prototype.datapoints._datapoint import Datapoint
+        from torchvision.datapoints import wrap_dataset_for_transforms_v2
+        from torchvision.datapoints._datapoint import Datapoint
 
         try:
             with self.create_dataset(config) as (dataset, _):
@@ -596,12 +593,13 @@ class DatasetTestCase(unittest.TestCase):
                 wrapped_sample = wrapped_dataset[0]
                 assert tree_any(lambda item: isinstance(item, (Datapoint, PIL.Image.Image)), wrapped_sample)
         except TypeError as error:
-            if str(error).startswith(f"No wrapper exists for dataset class {type(dataset).__name__}"):
-                return
+            msg = f"No wrapper exists for dataset class {type(dataset).__name__}"
+            if str(error).startswith(msg):
+                pytest.skip(msg)
             raise error
         except RuntimeError as error:
             if "currently not supported by this wrapper" in str(error):
-                return
+                pytest.skip("Config is currently not supported by this wrapper")
             raise error
 
 

test/prototype_common_utils.py

@@ -12,12 +12,13 @@ import PIL.Image
 import pytest
 import torch
 import torch.testing
+import torchvision.prototype.datapoints as proto_datapoints
 from datasets_utils import combinations_grid
 from torch.nn.functional import one_hot
 from torch.testing._comparison import BooleanPair, NonePair, not_close_error_metas, NumberPair, TensorLikePair
-from torchvision.prototype import datapoints
-from torchvision.prototype.transforms.functional import convert_dtype_image_tensor, to_image_tensor
+from torchvision import datapoints
 from torchvision.transforms.functional_tensor import _max_value as get_max_value
+from torchvision.transforms.v2.functional import convert_dtype_image_tensor, to_image_tensor
 
 __all__ = [
     "assert_close",
@@ -457,7 +458,7 @@ def make_label_loader(*, extra_dims=(), categories=None, dtype=torch.int64):
         # The idiom `make_tensor(..., dtype=torch.int64).to(dtype)` is intentional to only get integer values,
         # regardless of the requested dtype, e.g. 0 or 0.0 rather than 0 or 0.123
         data = torch.testing.make_tensor(shape, low=0, high=num_categories, dtype=torch.int64, device=device).to(dtype)
-        return datapoints.Label(data, categories=categories)
+        return proto_datapoints.Label(data, categories=categories)
 
     return LabelLoader(fn, shape=extra_dims, dtype=dtype, categories=categories)
 
@@ -481,7 +482,7 @@ def make_one_hot_label_loader(*, categories=None, extra_dims=(), dtype=torch.int
             # since `one_hot` only supports int64
             label = make_label_loader(extra_dims=extra_dims, categories=num_categories, dtype=torch.int64).load(device)
             data = one_hot(label, num_classes=num_categories).to(dtype)
-        return datapoints.OneHotLabel(data, categories=categories)
+        return proto_datapoints.OneHotLabel(data, categories=categories)
 
     return OneHotLabelLoader(fn, shape=(*extra_dims, num_categories), dtype=dtype, categories=categories)
 

test/prototype_transforms_dispatcher_infos.py

 import collections.abc
 
 import pytest
-import torchvision.prototype.transforms.functional as F
+import torchvision.transforms.v2.functional as F
 from prototype_common_utils import InfoBase, TestMark
 from prototype_transforms_kernel_infos import KERNEL_INFOS, pad_xfail_jit_fill_condition
-from torchvision.prototype import datapoints
+from torchvision import datapoints
 
 __all__ = ["DispatcherInfo", "DISPATCHER_INFOS"]
 

test/prototype_transforms_kernel_infos.py

@@ -8,7 +8,7 @@ import PIL.Image
 import pytest
 import torch.testing
 import torchvision.ops
-import torchvision.prototype.transforms.functional as F
+import torchvision.transforms.v2.functional as F
 from datasets_utils import combinations_grid
 from prototype_common_utils import (
     ArgsKwargs,
@@ -28,7 +28,7 @@ from prototype_common_utils import (
     TestMark,
 )
 from torch.utils._pytree import tree_map
-from torchvision.prototype import datapoints
+from torchvision import datapoints
 from torchvision.transforms.functional_tensor import _max_value as get_max_value, _parse_pad_padding
 
 __all__ = ["KernelInfo", "KERNEL_INFOS"]
@@ -2383,19 +2383,18 @@ KERNEL_INFOS.extend(
 
 def sample_inputs_uniform_temporal_subsample_video():
     for video_loader in make_video_loaders(sizes=["random"], num_frames=[4]):
-        for temporal_dim in [-4, len(video_loader.shape) - 4]:
-            yield ArgsKwargs(video_loader, num_samples=2, temporal_dim=temporal_dim)
+        yield ArgsKwargs(video_loader, num_samples=2)
 
 
-def reference_uniform_temporal_subsample_video(x, num_samples, temporal_dim=-4):
+def reference_uniform_temporal_subsample_video(x, num_samples):
     # Copy-pasted from
     # https://github.com/facebookresearch/pytorchvideo/blob/c8d23d8b7e597586a9e2d18f6ed31ad8aa379a7a/pytorchvideo/transforms/functional.py#L19
-    t = x.shape[temporal_dim]
+    t = x.shape[-4]
     assert num_samples > 0 and t > 0
     # Sample by nearest neighbor interpolation if num_samples > t.
     indices = torch.linspace(0, t - 1, num_samples)
     indices = torch.clamp(indices, 0, t - 1).long()
-    return torch.index_select(x, temporal_dim, indices)
+    return torch.index_select(x, -4, indices)
 
 
 def reference_inputs_uniform_temporal_subsample_video():
@@ -2410,12 +2409,5 @@ KERNEL_INFOS.append(
         sample_inputs_fn=sample_inputs_uniform_temporal_subsample_video,
         reference_fn=reference_uniform_temporal_subsample_video,
         reference_inputs_fn=reference_inputs_uniform_temporal_subsample_video,
-        test_marks=[
-            TestMark(
-                ("TestKernels", "test_batched_vs_single"),
-                pytest.mark.skip("Positive `temporal_dim` arguments are not equivalent for batched and single inputs"),
-                condition=lambda args_kwargs: args_kwargs.kwargs.get("temporal_dim") >= 0,
-            ),
-        ],
     )
 )

test/test_prototype_datapoints.py

@@ -5,8 +5,8 @@ import torch
 
 from PIL import Image
 
-from torchvision import datasets
-from torchvision.prototype import datapoints
+from torchvision import datapoints, datasets
+from torchvision.prototype import datapoints as proto_datapoints
 
 
 @pytest.mark.parametrize(
@@ -24,38 +24,38 @@ from torchvision.prototype import datapoints
     ],
 )
 def test_new_requires_grad(data, input_requires_grad, expected_requires_grad):
-    datapoint = datapoints.Label(data, requires_grad=input_requires_grad)
+    datapoint = proto_datapoints.Label(data, requires_grad=input_requires_grad)
     assert datapoint.requires_grad is expected_requires_grad
 
 
 def test_isinstance():
     assert isinstance(
-        datapoints.Label([0, 1, 0], categories=["foo", "bar"]),
+        proto_datapoints.Label([0, 1, 0], categories=["foo", "bar"]),
         torch.Tensor,
     )
 
 
 def test_wrapping_no_copy():
     tensor = torch.tensor([0, 1, 0], dtype=torch.int64)
-    label = datapoints.Label(tensor, categories=["foo", "bar"])
+    label = proto_datapoints.Label(tensor, categories=["foo", "bar"])
 
     assert label.data_ptr() == tensor.data_ptr()
 
 
 def test_to_wrapping():
     tensor = torch.tensor([0, 1, 0], dtype=torch.int64)
-    label = datapoints.Label(tensor, categories=["foo", "bar"])
+    label = proto_datapoints.Label(tensor, categories=["foo", "bar"])
 
     label_to = label.to(torch.int32)
 
-    assert type(label_to) is datapoints.Label
+    assert type(label_to) is proto_datapoints.Label
     assert label_to.dtype is torch.int32
     assert label_to.categories is label.categories
 
 
 def test_to_datapoint_reference():
     tensor = torch.tensor([0, 1, 0], dtype=torch.int64)
-    label = datapoints.Label(tensor, categories=["foo", "bar"]).to(torch.int32)
+    label = proto_datapoints.Label(tensor, categories=["foo", "bar"]).to(torch.int32)
 
     tensor_to = tensor.to(label)
 
@@ -65,31 +65,31 @@ def test_to_datapoint_reference():
 
 def test_clone_wrapping():
     tensor = torch.tensor([0, 1, 0], dtype=torch.int64)
-    label = datapoints.Label(tensor, categories=["foo", "bar"])
+    label = proto_datapoints.Label(tensor, categories=["foo", "bar"])
 
     label_clone = label.clone()
 
-    assert type(label_clone) is datapoints.Label
+    assert type(label_clone) is proto_datapoints.Label
     assert label_clone.data_ptr() != label.data_ptr()
     assert label_clone.categories is label.categories
 
 
 def test_requires_grad__wrapping():
     tensor = torch.tensor([0, 1, 0], dtype=torch.float32)
-    label = datapoints.Label(tensor, categories=["foo", "bar"])
+    label = proto_datapoints.Label(tensor, categories=["foo", "bar"])
 
     assert not label.requires_grad
 
     label_requires_grad = label.requires_grad_(True)
 
-    assert type(label_requires_grad) is datapoints.Label
+    assert type(label_requires_grad) is proto_datapoints.Label
     assert label.requires_grad
     assert label_requires_grad.requires_grad
 
 
 def test_other_op_no_wrapping():
     tensor = torch.tensor([0, 1, 0], dtype=torch.int64)
-    label = datapoints.Label(tensor, categories=["foo", "bar"])
+    label = proto_datapoints.Label(tensor, categories=["foo", "bar"])
 
     # any operation besides .to() and .clone() will do here
     output = label * 2
@@ -107,33 +107,33 @@ def test_other_op_no_wrapping():
 )
 def test_no_tensor_output_op_no_wrapping(op):
     tensor = torch.tensor([0, 1, 0], dtype=torch.int64)
-    label = datapoints.Label(tensor, categories=["foo", "bar"])
+    label = proto_datapoints.Label(tensor, categories=["foo", "bar"])
 
     output = op(label)
 
-    assert type(output) is not datapoints.Label
+    assert type(output) is not proto_datapoints.Label
 
 
 def test_inplace_op_no_wrapping():
     tensor = torch.tensor([0, 1, 0], dtype=torch.int64)
-    label = datapoints.Label(tensor, categories=["foo", "bar"])
+    label = proto_datapoints.Label(tensor, categories=["foo", "bar"])
 
     output = label.add_(0)
 
     assert type(output) is torch.Tensor
-    assert type(label) is datapoints.Label
+    assert type(label) is proto_datapoints.Label
 
 
 def test_wrap_like():
     tensor = torch.tensor([0, 1, 0], dtype=torch.int64)
-    label = datapoints.Label(tensor, categories=["foo", "bar"])
+    label = proto_datapoints.Label(tensor, categories=["foo", "bar"])
 
     # any operation besides .to() and .clone() will do here
     output = label * 2
 
-    label_new = datapoints.Label.wrap_like(label, output)
+    label_new = proto_datapoints.Label.wrap_like(label, output)
 
-    assert type(label_new) is datapoints.Label
+    assert type(label_new) is proto_datapoints.Label
     assert label_new.data_ptr() == output.data_ptr()
     assert label_new.categories is label.categories
 

test/test_prototype_datasets_builtin.py

@@ -5,8 +5,8 @@ from pathlib import Path
 
 import pytest
 import torch
+import torchvision.transforms.v2 as transforms
 
-import torchvision.prototype.transforms.utils
 from builtin_dataset_mocks import DATASET_MOCKS, parametrize_dataset_mocks
 from torch.testing._comparison import not_close_error_metas, ObjectPair, TensorLikePair
 
@@ -19,10 +19,13 @@ from torch.utils.data.graph_settings import get_all_graph_pipes
 from torchdata.dataloader2.graph.utils import traverse_dps
 from torchdata.datapipes.iter import ShardingFilter, Shuffler
 from torchdata.datapipes.utils import StreamWrapper
+from torchvision import datapoints
 from torchvision._utils import sequence_to_str
-from torchvision.prototype import datapoints, datasets, transforms
+from torchvision.prototype import datasets
+from torchvision.prototype.datapoints import Label
 from torchvision.prototype.datasets.utils import EncodedImage
 from torchvision.prototype.datasets.utils._internal import INFINITE_BUFFER_SIZE
+from torchvision.transforms.v2.utils import is_simple_tensor
 
 
 def assert_samples_equal(*args, msg=None, **kwargs):
@@ -141,9 +144,7 @@ class TestCommon:
         dataset, _ = dataset_mock.load(config)
         sample = next_consume(iter(dataset))
 
-        simple_tensors = {
-            key for key, value in sample.items() if torchvision.prototype.transforms.utils.is_simple_tensor(value)
-        }
+        simple_tensors = {key for key, value in sample.items() if is_simple_tensor(value)}
 
         if simple_tensors and not any(
             isinstance(item, (datapoints.Image, datapoints.Video, EncodedImage)) for item in sample.values()
@@ -276,6 +277,6 @@ class TestUSPS:
             assert "label" in sample
 
             assert isinstance(sample["image"], datapoints.Image)
-            assert isinstance(sample["label"], datapoints.Label)
+            assert isinstance(sample["label"], Label)
 
             assert sample["image"].shape == (1, 16, 16)

test/test_prototype_transforms.py

@@ -10,8 +10,11 @@ import numpy as np
 import PIL.Image
 import pytest
 import torch
+import torchvision.prototype.datapoints as proto_datapoints
+import torchvision.prototype.transforms as proto_transforms
+import torchvision.transforms.v2 as transforms
 
-import torchvision.prototype.transforms.utils
+import torchvision.transforms.v2.utils
 from common_utils import cpu_and_gpu
 from prototype_common_utils import (
     assert_equal,
@@ -28,11 +31,12 @@ from prototype_common_utils import (
     make_videos,
 )
 from torch.utils._pytree import tree_flatten, tree_unflatten
+from torchvision import datapoints
 from torchvision.ops.boxes import box_iou
-from torchvision.prototype import datapoints, transforms
-from torchvision.prototype.transforms import functional as F
-from torchvision.prototype.transforms.utils import check_type, is_simple_tensor, query_chw
 from torchvision.transforms.functional import InterpolationMode, pil_to_tensor, to_pil_image
+from torchvision.transforms.v2 import functional as F
+from torchvision.transforms.v2._utils import _convert_fill_arg
+from torchvision.transforms.v2.utils import check_type, is_simple_tensor, query_chw
 
 BATCH_EXTRA_DIMS = [extra_dims for extra_dims in DEFAULT_EXTRA_DIMS if extra_dims]
 
@@ -281,8 +285,8 @@ class TestSmoke:
                 ],
             )
             for transform in [
-                transforms.RandomMixup(alpha=1.0),
-                transforms.RandomCutmix(alpha=1.0),
+                proto_transforms.RandomMixup(alpha=1.0),
+                proto_transforms.RandomCutmix(alpha=1.0),
             ]
         ]
     )
@@ -563,7 +567,7 @@ class TestPad:
     def test__transform(self, padding, fill, padding_mode, mocker):
         transform = transforms.Pad(padding, fill=fill, padding_mode=padding_mode)
 
-        fn = mocker.patch("torchvision.prototype.transforms.functional.pad")
+        fn = mocker.patch("torchvision.transforms.v2.functional.pad")
         inpt = mocker.MagicMock(spec=datapoints.Image)
         _ = transform(inpt)
 
@@ -576,7 +580,7 @@ class TestPad:
     def test__transform_image_mask(self, fill, mocker):
         transform = transforms.Pad(1, fill=fill, padding_mode="constant")
 
-        fn = mocker.patch("torchvision.prototype.transforms.functional.pad")
+        fn = mocker.patch("torchvision.transforms.v2.functional.pad")
         image = datapoints.Image(torch.rand(3, 32, 32))
         mask = datapoints.Mask(torch.randint(0, 5, size=(32, 32)))
         inpt = [image, mask]
@@ -634,7 +638,7 @@ class TestRandomZoomOut:
 
         transform = transforms.RandomZoomOut(fill=fill, side_range=side_range, p=1)
 
-        fn = mocker.patch("torchvision.prototype.transforms.functional.pad")
+        fn = mocker.patch("torchvision.transforms.v2.functional.pad")
         # vfdev-5, Feature Request: let's store params as Transform attribute
         # This could be also helpful for users
         # Otherwise, we can mock transform._get_params
@@ -651,7 +655,7 @@ class TestRandomZoomOut:
     def test__transform_image_mask(self, fill, mocker):
         transform = transforms.RandomZoomOut(fill=fill, p=1.0)
 
-        fn = mocker.patch("torchvision.prototype.transforms.functional.pad")
+        fn = mocker.patch("torchvision.transforms.v2.functional.pad")
         image = datapoints.Image(torch.rand(3, 32, 32))
         mask = datapoints.Mask(torch.randint(0, 5, size=(32, 32)))
         inpt = [image, mask]
@@ -724,7 +728,7 @@ class TestRandomRotation:
         else:
             assert transform.degrees == [float(-degrees), float(degrees)]
 
-        fn = mocker.patch("torchvision.prototype.transforms.functional.rotate")
+        fn = mocker.patch("torchvision.transforms.v2.functional.rotate")
         inpt = mocker.MagicMock(spec=datapoints.Image)
         # vfdev-5, Feature Request: let's store params as Transform attribute
         # This could be also helpful for users
@@ -859,7 +863,7 @@ class TestRandomAffine:
         else:
             assert transform.degrees == [float(-degrees), float(degrees)]
 
-        fn = mocker.patch("torchvision.prototype.transforms.functional.affine")
+        fn = mocker.patch("torchvision.transforms.v2.functional.affine")
         inpt = mocker.MagicMock(spec=datapoints.Image)
         inpt.num_channels = 3
         inpt.spatial_size = (24, 32)
@@ -964,8 +968,8 @@ class TestRandomCrop:
             )
         else:
             expected.spatial_size = inpt.spatial_size
-        _ = mocker.patch("torchvision.prototype.transforms.functional.pad", return_value=expected)
-        fn_crop = mocker.patch("torchvision.prototype.transforms.functional.crop")
+        _ = mocker.patch("torchvision.transforms.v2.functional.pad", return_value=expected)
+        fn_crop = mocker.patch("torchvision.transforms.v2.functional.crop")
 
         # vfdev-5, Feature Request: let's store params as Transform attribute
         # This could be also helpful for users
@@ -1036,7 +1040,7 @@ class TestGaussianBlur:
         else:
             assert transform.sigma == [sigma, sigma]
 
-        fn = mocker.patch("torchvision.prototype.transforms.functional.gaussian_blur")
+        fn = mocker.patch("torchvision.transforms.v2.functional.gaussian_blur")
         inpt = mocker.MagicMock(spec=datapoints.Image)
         inpt.num_channels = 3
         inpt.spatial_size = (24, 32)
@@ -1068,7 +1072,7 @@ class TestRandomColorOp:
     def test__transform(self, p, transform_cls, func_op_name, kwargs, mocker):
         transform = transform_cls(p=p, **kwargs)
 
-        fn = mocker.patch(f"torchvision.prototype.transforms.functional.{func_op_name}")
+        fn = mocker.patch(f"torchvision.transforms.v2.functional.{func_op_name}")
         inpt = mocker.MagicMock(spec=datapoints.Image)
         _ = transform(inpt)
         if p > 0.0:
@@ -1104,7 +1108,7 @@ class TestRandomPerspective:
         fill = 12
         transform = transforms.RandomPerspective(distortion_scale, fill=fill, interpolation=interpolation)
 
-        fn = mocker.patch("torchvision.prototype.transforms.functional.perspective")
+        fn = mocker.patch("torchvision.transforms.v2.functional.perspective")
         inpt = mocker.MagicMock(spec=datapoints.Image)
         inpt.num_channels = 3
         inpt.spatial_size = (24, 32)
@@ -1178,7 +1182,7 @@ class TestElasticTransform:
         else:
             assert transform.sigma == sigma
 
-        fn = mocker.patch("torchvision.prototype.transforms.functional.elastic")
+        fn = mocker.patch("torchvision.transforms.v2.functional.elastic")
         inpt = mocker.MagicMock(spec=datapoints.Image)
         inpt.num_channels = 3
         inpt.spatial_size = (24, 32)
@@ -1251,13 +1255,13 @@ class TestRandomErasing:
         w_sentinel = mocker.MagicMock()
         v_sentinel = mocker.MagicMock()
         mocker.patch(
-            "torchvision.prototype.transforms._augment.RandomErasing._get_params",
+            "torchvision.transforms.v2._augment.RandomErasing._get_params",
             return_value=dict(i=i_sentinel, j=j_sentinel, h=h_sentinel, w=w_sentinel, v=v_sentinel),
         )
 
         inpt_sentinel = mocker.MagicMock()
 
-        mock = mocker.patch("torchvision.prototype.transforms._augment.F.erase")
+        mock = mocker.patch("torchvision.transforms.v2._augment.F.erase")
         output = transform(inpt_sentinel)
 
         if p:
@@ -1300,7 +1304,7 @@ class TestToImageTensor:
     )
     def test__transform(self, inpt_type, mocker):
         fn = mocker.patch(
-            "torchvision.prototype.transforms.functional.to_image_tensor",
+            "torchvision.transforms.v2.functional.to_image_tensor",
             return_value=torch.rand(1, 3, 8, 8),
         )
 
@@ -1319,7 +1323,7 @@ class TestToImagePIL:
         [torch.Tensor, PIL.Image.Image, datapoints.Image, np.ndarray, datapoints.BoundingBox, str, int],
     )
     def test__transform(self, inpt_type, mocker):
-        fn = mocker.patch("torchvision.prototype.transforms.functional.to_image_pil")
+        fn = mocker.patch("torchvision.transforms.v2.functional.to_image_pil")
 
         inpt = mocker.MagicMock(spec=inpt_type)
         transform = transforms.ToImagePIL()
@@ -1336,7 +1340,7 @@ class TestToPILImage:
         [torch.Tensor, PIL.Image.Image, datapoints.Image, np.ndarray, datapoints.BoundingBox, str, int],
     )
     def test__transform(self, inpt_type, mocker):
-        fn = mocker.patch("torchvision.prototype.transforms.functional.to_image_pil")
+        fn = mocker.patch("torchvision.transforms.v2.functional.to_image_pil")
 
         inpt = mocker.MagicMock(spec=inpt_type)
         transform = transforms.ToPILImage()
@@ -1443,7 +1447,7 @@ class TestRandomIoUCrop:
         transform = transforms.RandomIoUCrop(sampler_options=[2.0])
         image = datapoints.Image(torch.rand(1, 3, 4, 4))
         bboxes = datapoints.BoundingBox(torch.tensor([[1, 1, 2, 2]]), format="XYXY", spatial_size=(4, 4))
-        label = datapoints.Label(torch.tensor([1]))
+        label = proto_datapoints.Label(torch.tensor([1]))
         sample = [image, bboxes, label]
         # Let's mock transform._get_params to control the output:
         transform._get_params = mocker.MagicMock(return_value={})
@@ -1454,7 +1458,7 @@ class TestRandomIoUCrop:
         transform = transforms.RandomIoUCrop()
         with pytest.raises(
             TypeError,
-            match="requires input sample to contain Images or PIL Images, BoundingBoxes and Labels or OneHotLabels",
+            match="requires input sample to contain tensor or PIL images and bounding boxes",
         ):
             transform(torch.tensor(0))
 
@@ -1463,13 +1467,11 @@ class TestRandomIoUCrop:
 
         image = datapoints.Image(torch.rand(3, 32, 24))
         bboxes = make_bounding_box(format="XYXY", spatial_size=(32, 24), extra_dims=(6,))
-        label = datapoints.Label(torch.randint(0, 10, size=(6,)))
-        ohe_label = datapoints.OneHotLabel(torch.zeros(6, 10).scatter_(1, label.unsqueeze(1), 1))
         masks = make_detection_mask((32, 24), num_objects=6)
 
-        sample = [image, bboxes, label, ohe_label, masks]
+        sample = [image, bboxes, masks]
 
-        fn = mocker.patch("torchvision.prototype.transforms.functional.crop", side_effect=lambda x, **params: x)
+        fn = mocker.patch("torchvision.transforms.v2.functional.crop", side_effect=lambda x, **params: x)
         is_within_crop_area = torch.tensor([0, 1, 0, 1, 0, 1], dtype=torch.bool)
 
         params = dict(top=1, left=2, height=12, width=12, is_within_crop_area=is_within_crop_area)
@@ -1493,17 +1495,7 @@ class TestRandomIoUCrop:
         assert isinstance(output_bboxes, datapoints.BoundingBox)
         assert len(output_bboxes) == expected_within_targets
 
-        # check labels
-        output_label = output[2]
-        assert isinstance(output_label, datapoints.Label)
-        assert len(output_label) == expected_within_targets
-        torch.testing.assert_close(output_label, label[is_within_crop_area])
-
-        output_ohe_label = output[3]
-        assert isinstance(output_ohe_label, datapoints.OneHotLabel)
-        torch.testing.assert_close(output_ohe_label, ohe_label[is_within_crop_area])
-
-        output_masks = output[4]
+        output_masks = output[2]
         assert isinstance(output_masks, datapoints.Mask)
         assert len(output_masks) == expected_within_targets
 
@@ -1545,12 +1537,12 @@ class TestScaleJitter:
 
         size_sentinel = mocker.MagicMock()
         mocker.patch(
-            "torchvision.prototype.transforms._geometry.ScaleJitter._get_params", return_value=dict(size=size_sentinel)
+            "torchvision.transforms.v2._geometry.ScaleJitter._get_params", return_value=dict(size=size_sentinel)
         )
 
         inpt_sentinel = mocker.MagicMock()
 
-        mock = mocker.patch("torchvision.prototype.transforms._geometry.F.resize")
+        mock = mocker.patch("torchvision.transforms.v2._geometry.F.resize")
         transform(inpt_sentinel)
 
         mock.assert_called_once_with(
@@ -1592,13 +1584,13 @@ class TestRandomShortestSize:
 
         size_sentinel = mocker.MagicMock()
         mocker.patch(
-            "torchvision.prototype.transforms._geometry.RandomShortestSize._get_params",
+            "torchvision.transforms.v2._geometry.RandomShortestSize._get_params",
             return_value=dict(size=size_sentinel),
         )
 
         inpt_sentinel = mocker.MagicMock()
 
-        mock = mocker.patch("torchvision.prototype.transforms._geometry.F.resize")
+        mock = mocker.patch("torchvision.transforms.v2._geometry.F.resize")
         transform(inpt_sentinel)
 
         mock.assert_called_once_with(
@@ -1613,13 +1605,13 @@ class TestSimpleCopyPaste:
         return mocker.MagicMock(spec=image_type)
 
     def test__extract_image_targets_assertion(self, mocker):
-        transform = transforms.SimpleCopyPaste()
+        transform = proto_transforms.SimpleCopyPaste()
 
         flat_sample = [
             # images, batch size = 2
             self.create_fake_image(mocker, datapoints.Image),
             # labels, bboxes, masks
-            mocker.MagicMock(spec=datapoints.Label),
+            mocker.MagicMock(spec=proto_datapoints.Label),
             mocker.MagicMock(spec=datapoints.BoundingBox),
             mocker.MagicMock(spec=datapoints.Mask),
             # labels, bboxes, masks
@@ -1631,9 +1623,9 @@ class TestSimpleCopyPaste:
             transform._extract_image_targets(flat_sample)
 
     @pytest.mark.parametrize("image_type", [datapoints.Image, PIL.Image.Image, torch.Tensor])
-    @pytest.mark.parametrize("label_type", [datapoints.Label, datapoints.OneHotLabel])
+    @pytest.mark.parametrize("label_type", [proto_datapoints.Label, proto_datapoints.OneHotLabel])
     def test__extract_image_targets(self, image_type, label_type, mocker):
-        transform = transforms.SimpleCopyPaste()
+        transform = proto_transforms.SimpleCopyPaste()
 
         flat_sample = [
             # images, batch size = 2
@@ -1669,7 +1661,7 @@ class TestSimpleCopyPaste:
                 assert isinstance(target[key], type_)
                 assert target[key] in flat_sample
 
-    @pytest.mark.parametrize("label_type", [datapoints.Label, datapoints.OneHotLabel])
+    @pytest.mark.parametrize("label_type", [proto_datapoints.Label, proto_datapoints.OneHotLabel])
     def test__copy_paste(self, label_type):
         image = 2 * torch.ones(3, 32, 32)
         masks = torch.zeros(2, 32, 32)
@@ -1679,7 +1671,7 @@ class TestSimpleCopyPaste:
         blending = True
         resize_interpolation = InterpolationMode.BILINEAR
         antialias = None
-        if label_type == datapoints.OneHotLabel:
+        if label_type == proto_datapoints.OneHotLabel:
             labels = torch.nn.functional.one_hot(labels, num_classes=5)
         target = {
             "boxes": datapoints.BoundingBox(
@@ -1694,7 +1686,7 @@ class TestSimpleCopyPaste:
         paste_masks[0, 13:19, 12:18] = 1
         paste_masks[1, 15:19, 1:8] = 1
         paste_labels = torch.tensor([3, 4])
-        if label_type == datapoints.OneHotLabel:
+        if label_type == proto_datapoints.OneHotLabel:
             paste_labels = torch.nn.functional.one_hot(paste_labels, num_classes=5)
         paste_target = {
             "boxes": datapoints.BoundingBox(
@@ -1704,7 +1696,7 @@ class TestSimpleCopyPaste:
             "labels": label_type(paste_labels),
         }
 
-        transform = transforms.SimpleCopyPaste()
+        transform = proto_transforms.SimpleCopyPaste()
         random_selection = torch.tensor([0, 1])
         output_image, output_target = transform._copy_paste(
             image, target, paste_image, paste_target, random_selection, blending, resize_interpolation, antialias
@@ -1716,7 +1708,7 @@ class TestSimpleCopyPaste:
         torch.testing.assert_close(output_target["boxes"][2:, :], paste_target["boxes"])
 
         expected_labels = torch.tensor([1, 2, 3, 4])
-        if label_type == datapoints.OneHotLabel:
+        if label_type == proto_datapoints.OneHotLabel:
             expected_labels = torch.nn.functional.one_hot(expected_labels, num_classes=5)
         torch.testing.assert_close(output_target["labels"], label_type(expected_labels))
 
@@ -1731,7 +1723,7 @@ class TestFixedSizeCrop:
         batch_shape = (10,)
         spatial_size = (11, 5)
 
-        transform = transforms.FixedSizeCrop(size=crop_size)
+        transform = proto_transforms.FixedSizeCrop(size=crop_size)
 
         flat_inputs = [
             make_image(size=spatial_size, color_space="RGB"),
@@ -1759,9 +1751,8 @@ class TestFixedSizeCrop:
         fill_sentinel = 12
         padding_mode_sentinel = mocker.MagicMock()
 
-        transform = transforms.FixedSizeCrop((-1, -1), fill=fill_sentinel, padding_mode=padding_mode_sentinel)
+        transform = proto_transforms.FixedSizeCrop((-1, -1), fill=fill_sentinel, padding_mode=padding_mode_sentinel)
         transform._transformed_types = (mocker.MagicMock,)
-        mocker.patch("torchvision.prototype.transforms._geometry.has_all", return_value=True)
         mocker.patch("torchvision.prototype.transforms._geometry.has_any", return_value=True)
 
         needs_crop, needs_pad = needs
@@ -1810,7 +1801,7 @@ class TestFixedSizeCrop:
             if not needs_crop:
                 assert args[0] is inpt_sentinel
             assert args[1] is padding_sentinel
-            fill_sentinel = transforms._utils._convert_fill_arg(fill_sentinel)
+            fill_sentinel = _convert_fill_arg(fill_sentinel)
             assert kwargs == dict(fill=fill_sentinel, padding_mode=padding_mode_sentinel)
         else:
             mock_pad.assert_not_called()
@@ -1839,8 +1830,7 @@ class TestFixedSizeCrop:
         masks = make_detection_mask(size=spatial_size, extra_dims=(batch_size,))
         labels = make_label(extra_dims=(batch_size,))
 
-        transform = transforms.FixedSizeCrop((-1, -1))
-        mocker.patch("torchvision.prototype.transforms._geometry.has_all", return_value=True)
+        transform = proto_transforms.FixedSizeCrop((-1, -1))
         mocker.patch("torchvision.prototype.transforms._geometry.has_any", return_value=True)
 
         output = transform(
@@ -1877,8 +1867,7 @@ class TestFixedSizeCrop:
         )
         mock = mocker.patch("torchvision.prototype.transforms._geometry.F.clamp_bounding_box")
 
-        transform = transforms.FixedSizeCrop((-1, -1))
-        mocker.patch("torchvision.prototype.transforms._geometry.has_all", return_value=True)
+        transform = proto_transforms.FixedSizeCrop((-1, -1))
         mocker.patch("torchvision.prototype.transforms._geometry.has_any", return_value=True)
 
         transform(bounding_box)
@@ -1922,10 +1911,10 @@ class TestLinearTransformation:
 class TestLabelToOneHot:
     def test__transform(self):
         categories = ["apple", "pear", "pineapple"]
-        labels = datapoints.Label(torch.tensor([0, 1, 2, 1]), categories=categories)
-        transform = transforms.LabelToOneHot()
+        labels = proto_datapoints.Label(torch.tensor([0, 1, 2, 1]), categories=categories)
+        transform = proto_transforms.LabelToOneHot()
         ohe_labels = transform(labels)
-        assert isinstance(ohe_labels, datapoints.OneHotLabel)
+        assert isinstance(ohe_labels, proto_datapoints.OneHotLabel)
         assert ohe_labels.shape == (4, 3)
         assert ohe_labels.categories == labels.categories == categories
 
@@ -1956,13 +1945,13 @@ class TestRandomResize:
 
         size_sentinel = mocker.MagicMock()
         mocker.patch(
-            "torchvision.prototype.transforms._geometry.RandomResize._get_params",
+            "torchvision.transforms.v2._geometry.RandomResize._get_params",
             return_value=dict(size=size_sentinel),
         )
 
         inpt_sentinel = mocker.MagicMock()
 
-        mock_resize = mocker.patch("torchvision.prototype.transforms._geometry.F.resize")
+        mock_resize = mocker.patch("torchvision.transforms.v2._geometry.F.resize")
         transform(inpt_sentinel)
 
         mock_resize.assert_called_with(
@@ -2048,7 +2037,7 @@ class TestPermuteDimensions:
             int=0,
         )
 
-        transform = transforms.PermuteDimensions(dims)
+        transform = proto_transforms.PermuteDimensions(dims)
         transformed_sample = transform(sample)
 
         for key, value in sample.items():
@@ -2056,7 +2045,7 @@ class TestPermuteDimensions:
             transformed_value = transformed_sample[key]
 
             if check_type(
-                value, (datapoints.Image, torchvision.prototype.transforms.utils.is_simple_tensor, datapoints.Video)
+                value, (datapoints.Image, torchvision.transforms.v2.utils.is_simple_tensor, datapoints.Video)
             ):
                 if transform.dims.get(value_type) is not None:
                     assert transformed_value.permute(inverse_dims[value_type]).equal(value)
@@ -2067,14 +2056,14 @@ class TestPermuteDimensions:
     @pytest.mark.filterwarnings("error")
     def test_plain_tensor_call(self):
         tensor = torch.empty((2, 3, 4))
-        transform = transforms.PermuteDimensions(dims=(1, 2, 0))
+        transform = proto_transforms.PermuteDimensions(dims=(1, 2, 0))
 
         assert transform(tensor).shape == (3, 4, 2)
 
     @pytest.mark.parametrize("other_type", [datapoints.Image, datapoints.Video])
     def test_plain_tensor_warning(self, other_type):
         with pytest.warns(UserWarning, match=re.escape("`torch.Tensor` will *not* be transformed")):
-            transforms.PermuteDimensions(dims={torch.Tensor: (0, 1), other_type: (1, 0)})
+            proto_transforms.PermuteDimensions(dims={torch.Tensor: (0, 1), other_type: (1, 0)})
 
 
 class TestTransposeDimensions:
@@ -2094,7 +2083,7 @@ class TestTransposeDimensions:
             int=0,
         )
 
-        transform = transforms.TransposeDimensions(dims)
+        transform = proto_transforms.TransposeDimensions(dims)
         transformed_sample = transform(sample)
 
         for key, value in sample.items():
@@ -2103,7 +2092,7 @@ class TestTransposeDimensions:
 
             transposed_dims = transform.dims.get(value_type)
             if check_type(
-                value, (datapoints.Image, torchvision.prototype.transforms.utils.is_simple_tensor, datapoints.Video)
+                value, (datapoints.Image, torchvision.transforms.v2.utils.is_simple_tensor, datapoints.Video)
             ):
                 if transposed_dims is not None:
                     assert transformed_value.transpose(*transposed_dims).equal(value)
@@ -2114,14 +2103,14 @@ class TestTransposeDimensions:
     @pytest.mark.filterwarnings("error")
     def test_plain_tensor_call(self):
         tensor = torch.empty((2, 3, 4))
-        transform = transforms.TransposeDimensions(dims=(0, 2))
+        transform = proto_transforms.TransposeDimensions(dims=(0, 2))
 
         assert transform(tensor).shape == (4, 3, 2)
 
     @pytest.mark.parametrize("other_type", [datapoints.Image, datapoints.Video])
     def test_plain_tensor_warning(self, other_type):
         with pytest.warns(UserWarning, match=re.escape("`torch.Tensor` will *not* be transformed")):
-            transforms.TransposeDimensions(dims={torch.Tensor: (0, 1), other_type: (1, 0)})
+            proto_transforms.TransposeDimensions(dims={torch.Tensor: (0, 1), other_type: (1, 0)})
 
 
 class TestUniformTemporalSubsample:

test/test_prototype_transforms_consistency.py

@@ -12,6 +12,8 @@ import PIL.Image
 import pytest
 
 import torch
+import torchvision.prototype.transforms as prototype_transforms
+import torchvision.transforms.v2 as v2_transforms
 from prototype_common_utils import (
     ArgsKwargs,
     assert_close,
@@ -24,13 +26,13 @@ from prototype_common_utils import (
     make_segmentation_mask,
 )
 from torch import nn
-from torchvision import transforms as legacy_transforms
+from torchvision import datapoints, transforms as legacy_transforms
 from torchvision._utils import sequence_to_str
-from torchvision.prototype import datapoints, transforms as prototype_transforms
-from torchvision.prototype.transforms import functional as prototype_F
-from torchvision.prototype.transforms.functional import to_image_pil
-from torchvision.prototype.transforms.utils import query_spatial_size
+
 from torchvision.transforms import functional as legacy_F
+from torchvision.transforms.v2 import functional as prototype_F
+from torchvision.transforms.v2.functional import to_image_pil
+from torchvision.transforms.v2.utils import query_spatial_size
 
 DEFAULT_MAKE_IMAGES_KWARGS = dict(color_spaces=["RGB"], extra_dims=[(4,)])
 
@@ -71,7 +73,7 @@ LINEAR_TRANSFORMATION_MATRIX = torch.rand([LINEAR_TRANSFORMATION_MEAN.numel()] *
 
 CONSISTENCY_CONFIGS = [
     ConsistencyConfig(
-        prototype_transforms.Normalize,
+        v2_transforms.Normalize,
         legacy_transforms.Normalize,
         [
             ArgsKwargs(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
@@ -80,14 +82,14 @@ CONSISTENCY_CONFIGS = [
         make_images_kwargs=dict(DEFAULT_MAKE_IMAGES_KWARGS, dtypes=[torch.float]),
     ),
     ConsistencyConfig(
-        prototype_transforms.Resize,
+        v2_transforms.Resize,
         legacy_transforms.Resize,
         [
             NotScriptableArgsKwargs(32),
             ArgsKwargs([32]),
             ArgsKwargs((32, 29)),
-            ArgsKwargs((31, 28), interpolation=prototype_transforms.InterpolationMode.NEAREST),
-            ArgsKwargs((33, 26), interpolation=prototype_transforms.InterpolationMode.BICUBIC),
+            ArgsKwargs((31, 28), interpolation=v2_transforms.InterpolationMode.NEAREST),
+            ArgsKwargs((33, 26), interpolation=v2_transforms.InterpolationMode.BICUBIC),
             ArgsKwargs((30, 27), interpolation=PIL.Image.NEAREST),
             ArgsKwargs((35, 29), interpolation=PIL.Image.BILINEAR),
             ArgsKwargs((34, 25), interpolation=PIL.Image.BICUBIC),
@@ -100,7 +102,7 @@ CONSISTENCY_CONFIGS = [
         ],
     ),
     ConsistencyConfig(
-        prototype_transforms.CenterCrop,
+        v2_transforms.CenterCrop,
         legacy_transforms.CenterCrop,
         [
             ArgsKwargs(18),
@@ -108,7 +110,7 @@ CONSISTENCY_CONFIGS = [
         ],
     ),
     ConsistencyConfig(
-        prototype_transforms.FiveCrop,
+        v2_transforms.FiveCrop,
         legacy_transforms.FiveCrop,
         [
             ArgsKwargs(18),
@@ -117,7 +119,7 @@ CONSISTENCY_CONFIGS = [
         make_images_kwargs=dict(DEFAULT_MAKE_IMAGES_KWARGS, sizes=[(20, 19)]),
     ),
     ConsistencyConfig(
-        prototype_transforms.TenCrop,
+        v2_transforms.TenCrop,
         legacy_transforms.TenCrop,
         [
             ArgsKwargs(18),
@@ -127,7 +129,7 @@ CONSISTENCY_CONFIGS = [
         make_images_kwargs=dict(DEFAULT_MAKE_IMAGES_KWARGS, sizes=[(20, 19)]),
     ),
     ConsistencyConfig(
-        prototype_transforms.Pad,
+        v2_transforms.Pad,
         legacy_transforms.Pad,
         [
             NotScriptableArgsKwargs(3),
@@ -143,7 +145,7 @@ CONSISTENCY_CONFIGS = [
     ),
     *[
         ConsistencyConfig(
-            prototype_transforms.LinearTransformation,
+            v2_transforms.LinearTransformation,
             legacy_transforms.LinearTransformation,
             [
                 ArgsKwargs(LINEAR_TRANSFORMATION_MATRIX.to(matrix_dtype), LINEAR_TRANSFORMATION_MEAN.to(matrix_dtype)),
@@ -164,7 +166,7 @@ CONSISTENCY_CONFIGS = [
         ]
     ],
     ConsistencyConfig(
-        prototype_transforms.Grayscale,
+        v2_transforms.Grayscale,
         legacy_transforms.Grayscale,
         [
             ArgsKwargs(num_output_channels=1),
@@ -175,7 +177,7 @@ CONSISTENCY_CONFIGS = [
         closeness_kwargs=dict(rtol=None, atol=None),
     ),
     ConsistencyConfig(
-        prototype_transforms.ConvertDtype,
+        v2_transforms.ConvertDtype,
         legacy_transforms.ConvertImageDtype,
         [
             ArgsKwargs(torch.float16),
@@ -189,7 +191,7 @@ CONSISTENCY_CONFIGS = [
         closeness_kwargs=dict(rtol=None, atol=None),
     ),
     ConsistencyConfig(
-        prototype_transforms.ToPILImage,
+        v2_transforms.ToPILImage,
         legacy_transforms.ToPILImage,
         [NotScriptableArgsKwargs()],
         make_images_kwargs=dict(
@@ -204,7 +206,7 @@ CONSISTENCY_CONFIGS = [
         supports_pil=False,
     ),
     ConsistencyConfig(
-        prototype_transforms.Lambda,
+        v2_transforms.Lambda,
         legacy_transforms.Lambda,
         [
             NotScriptableArgsKwargs(lambda image: image / 2),
@@ -214,7 +216,7 @@ CONSISTENCY_CONFIGS = [
         supports_pil=False,
     ),
     ConsistencyConfig(
-        prototype_transforms.RandomHorizontalFlip,
+        v2_transforms.RandomHorizontalFlip,
         legacy_transforms.RandomHorizontalFlip,
         [
             ArgsKwargs(p=0),
@@ -222,7 +224,7 @@ CONSISTENCY_CONFIGS = [
         ],
     ),
     ConsistencyConfig(
-        prototype_transforms.RandomVerticalFlip,
+        v2_transforms.RandomVerticalFlip,
         legacy_transforms.RandomVerticalFlip,
         [
             ArgsKwargs(p=0),
@@ -230,7 +232,7 @@ CONSISTENCY_CONFIGS = [
         ],
     ),
     ConsistencyConfig(
-        prototype_transforms.RandomEqualize,
+        v2_transforms.RandomEqualize,
         legacy_transforms.RandomEqualize,
         [
             ArgsKwargs(p=0),
@@ -239,7 +241,7 @@ CONSISTENCY_CONFIGS = [
         make_images_kwargs=dict(DEFAULT_MAKE_IMAGES_KWARGS, dtypes=[torch.uint8]),
     ),
     ConsistencyConfig(
-        prototype_transforms.RandomInvert,
+        v2_transforms.RandomInvert,
         legacy_transforms.RandomInvert,
         [
             ArgsKwargs(p=0),
@@ -247,7 +249,7 @@ CONSISTENCY_CONFIGS = [
         ],
     ),
     ConsistencyConfig(
-        prototype_transforms.RandomPosterize,
+        v2_transforms.RandomPosterize,
         legacy_transforms.RandomPosterize,
         [
             ArgsKwargs(p=0, bits=5),
@@ -257,7 +259,7 @@ CONSISTENCY_CONFIGS = [
         make_images_kwargs=dict(DEFAULT_MAKE_IMAGES_KWARGS, dtypes=[torch.uint8]),
     ),
     ConsistencyConfig(
-        prototype_transforms.RandomSolarize,
+        v2_transforms.RandomSolarize,
         legacy_transforms.RandomSolarize,
         [
             ArgsKwargs(p=0, threshold=0.5),
@@ -267,7 +269,7 @@ CONSISTENCY_CONFIGS = [
     ),
     *[
         ConsistencyConfig(
-            prototype_transforms.RandomAutocontrast,
+            v2_transforms.RandomAutocontrast,
             legacy_transforms.RandomAutocontrast,
             [
                 ArgsKwargs(p=0),
@@ -279,7 +281,7 @@ CONSISTENCY_CONFIGS = [
         for dt, ckw in [(torch.uint8, dict(atol=1, rtol=0)), (torch.float32, dict(rtol=None, atol=None))]
     ],
     ConsistencyConfig(
-        prototype_transforms.RandomAdjustSharpness,
+        v2_transforms.RandomAdjustSharpness,
         legacy_transforms.RandomAdjustSharpness,
         [
             ArgsKwargs(p=0, sharpness_factor=0.5),
@@ -289,7 +291,7 @@ CONSISTENCY_CONFIGS = [
         closeness_kwargs={"atol": 1e-6, "rtol": 1e-6},
     ),
     ConsistencyConfig(
-        prototype_transforms.RandomGrayscale,
+        v2_transforms.RandomGrayscale,
         legacy_transforms.RandomGrayscale,
         [
             ArgsKwargs(p=0),
@@ -300,14 +302,14 @@ CONSISTENCY_CONFIGS = [
         closeness_kwargs=dict(rtol=None, atol=None),
     ),
     ConsistencyConfig(
-        prototype_transforms.RandomResizedCrop,
+        v2_transforms.RandomResizedCrop,
         legacy_transforms.RandomResizedCrop,
         [
             ArgsKwargs(16),
             ArgsKwargs(17, scale=(0.3, 0.7)),
             ArgsKwargs(25, ratio=(0.5, 1.5)),
-            ArgsKwargs((31, 28), interpolation=prototype_transforms.InterpolationMode.NEAREST),
-            ArgsKwargs((33, 26), interpolation=prototype_transforms.InterpolationMode.BICUBIC),
+            ArgsKwargs((31, 28), interpolation=v2_transforms.InterpolationMode.NEAREST),
+            ArgsKwargs((33, 26), interpolation=v2_transforms.InterpolationMode.BICUBIC),
             ArgsKwargs((31, 28), interpolation=PIL.Image.NEAREST),
             ArgsKwargs((33, 26), interpolation=PIL.Image.BICUBIC),
             ArgsKwargs((29, 32), antialias=False),
@@ -315,7 +317,7 @@ CONSISTENCY_CONFIGS = [
         ],
     ),
     ConsistencyConfig(
-        prototype_transforms.RandomErasing,
+        v2_transforms.RandomErasing,
         legacy_transforms.RandomErasing,
         [
             ArgsKwargs(p=0),
@@ -329,7 +331,7 @@ CONSISTENCY_CONFIGS = [
         supports_pil=False,
     ),
     ConsistencyConfig(
-        prototype_transforms.ColorJitter,
+        v2_transforms.ColorJitter,
         legacy_transforms.ColorJitter,
         [
             ArgsKwargs(),
@@ -347,7 +349,7 @@ CONSISTENCY_CONFIGS = [
     ),
     *[
         ConsistencyConfig(
-            prototype_transforms.ElasticTransform,
+            v2_transforms.ElasticTransform,
             legacy_transforms.ElasticTransform,
             [
                 ArgsKwargs(),
@@ -355,8 +357,8 @@ CONSISTENCY_CONFIGS = [
                 ArgsKwargs(alpha=(15.3, 27.2)),
                 ArgsKwargs(sigma=3.0),
                 ArgsKwargs(sigma=(2.5, 3.9)),
-                ArgsKwargs(interpolation=prototype_transforms.InterpolationMode.NEAREST),
-                ArgsKwargs(interpolation=prototype_transforms.InterpolationMode.BICUBIC),
+                ArgsKwargs(interpolation=v2_transforms.InterpolationMode.NEAREST),
+                ArgsKwargs(interpolation=v2_transforms.InterpolationMode.BICUBIC),
                 ArgsKwargs(interpolation=PIL.Image.NEAREST),
                 ArgsKwargs(interpolation=PIL.Image.BICUBIC),
                 ArgsKwargs(fill=1),
@@ -370,7 +372,7 @@ CONSISTENCY_CONFIGS = [
         for dt, ckw in [(torch.uint8, {"rtol": 1e-1, "atol": 1}), (torch.float32, {"rtol": 1e-2, "atol": 1e-3})]
     ],
     ConsistencyConfig(
-        prototype_transforms.GaussianBlur,
+        v2_transforms.GaussianBlur,
         legacy_transforms.GaussianBlur,
         [
             ArgsKwargs(kernel_size=3),
@@ -381,7 +383,7 @@ CONSISTENCY_CONFIGS = [
         closeness_kwargs={"rtol": 1e-5, "atol": 1e-5},
     ),
     ConsistencyConfig(
-        prototype_transforms.RandomAffine,
+        v2_transforms.RandomAffine,
         legacy_transforms.RandomAffine,
         [
             ArgsKwargs(degrees=30.0),
@@ -392,7 +394,7 @@ CONSISTENCY_CONFIGS = [
             ArgsKwargs(degrees=0.0, shear=(8, 17)),
             ArgsKwargs(degrees=0.0, shear=(4, 5, 4, 13)),
             ArgsKwargs(degrees=(-20.0, 10.0), translate=(0.4, 0.6), scale=(0.3, 0.8), shear=(4, 5, 4, 13)),
-            ArgsKwargs(degrees=30.0, interpolation=prototype_transforms.InterpolationMode.NEAREST),
+            ArgsKwargs(degrees=30.0, interpolation=v2_transforms.InterpolationMode.NEAREST),
             ArgsKwargs(degrees=30.0, interpolation=PIL.Image.NEAREST),
             ArgsKwargs(degrees=30.0, fill=1),
             ArgsKwargs(degrees=30.0, fill=(2, 3, 4)),
@@ -401,7 +403,7 @@ CONSISTENCY_CONFIGS = [
         removed_params=["fillcolor", "resample"],
     ),
     ConsistencyConfig(
-        prototype_transforms.RandomCrop,
+        v2_transforms.RandomCrop,
         legacy_transforms.RandomCrop,
         [
             ArgsKwargs(12),
@@ -421,13 +423,13 @@ CONSISTENCY_CONFIGS = [
         make_images_kwargs=dict(DEFAULT_MAKE_IMAGES_KWARGS, sizes=[(26, 26), (18, 33), (29, 22)]),
     ),
     ConsistencyConfig(
-        prototype_transforms.RandomPerspective,
+        v2_transforms.RandomPerspective,
         legacy_transforms.RandomPerspective,
         [
             ArgsKwargs(p=0),
             ArgsKwargs(p=1),
             ArgsKwargs(p=1, distortion_scale=0.3),
-            ArgsKwargs(p=1, distortion_scale=0.2, interpolation=prototype_transforms.InterpolationMode.NEAREST),
+            ArgsKwargs(p=1, distortion_scale=0.2, interpolation=v2_transforms.InterpolationMode.NEAREST),
             ArgsKwargs(p=1, distortion_scale=0.2, interpolation=PIL.Image.NEAREST),
             ArgsKwargs(p=1, distortion_scale=0.1, fill=1),
             ArgsKwargs(p=1, distortion_scale=0.4, fill=(1, 2, 3)),
@@ -435,12 +437,12 @@ CONSISTENCY_CONFIGS = [
         closeness_kwargs={"atol": None, "rtol": None},
     ),
     ConsistencyConfig(
-        prototype_transforms.RandomRotation,
+        v2_transforms.RandomRotation,
         legacy_transforms.RandomRotation,
         [
             ArgsKwargs(degrees=30.0),
             ArgsKwargs(degrees=(-20.0, 10.0)),
-            ArgsKwargs(degrees=30.0, interpolation=prototype_transforms.InterpolationMode.BILINEAR),
+            ArgsKwargs(degrees=30.0, interpolation=v2_transforms.InterpolationMode.BILINEAR),
             ArgsKwargs(degrees=30.0, interpolation=PIL.Image.BILINEAR),
             ArgsKwargs(degrees=30.0, expand=True),
             ArgsKwargs(degrees=30.0, center=(0, 0)),
@@ -450,43 +452,43 @@ CONSISTENCY_CONFIGS = [
         removed_params=["resample"],
     ),
     ConsistencyConfig(
-        prototype_transforms.PILToTensor,
+        v2_transforms.PILToTensor,
         legacy_transforms.PILToTensor,
     ),
     ConsistencyConfig(
-        prototype_transforms.ToTensor,
+        v2_transforms.ToTensor,
         legacy_transforms.ToTensor,
     ),
     ConsistencyConfig(
-        prototype_transforms.Compose,
+        v2_transforms.Compose,
         legacy_transforms.Compose,
     ),
     ConsistencyConfig(
-        prototype_transforms.RandomApply,
+        v2_transforms.RandomApply,
         legacy_transforms.RandomApply,
     ),
     ConsistencyConfig(
-        prototype_transforms.RandomChoice,
+        v2_transforms.RandomChoice,
         legacy_transforms.RandomChoice,
     ),
     ConsistencyConfig(
-        prototype_transforms.RandomOrder,
+        v2_transforms.RandomOrder,
         legacy_transforms.RandomOrder,
     ),
     ConsistencyConfig(
-        prototype_transforms.AugMix,
+        v2_transforms.AugMix,
         legacy_transforms.AugMix,
     ),
     ConsistencyConfig(
-        prototype_transforms.AutoAugment,
+        v2_transforms.AutoAugment,
         legacy_transforms.AutoAugment,
     ),
     ConsistencyConfig(
-        prototype_transforms.RandAugment,
+        v2_transforms.RandAugment,
         legacy_transforms.RandAugment,
     ),
     ConsistencyConfig(
-        prototype_transforms.TrivialAugmentWide,
+        v2_transforms.TrivialAugmentWide,
         legacy_transforms.TrivialAugmentWide,
     ),
 ]
@@ -680,19 +682,19 @@ get_params_parametrization = pytest.mark.parametrize(
             id=transform_cls.__name__,
         )
         for transform_cls, get_params_args_kwargs in [
-            (prototype_transforms.RandomResizedCrop, ArgsKwargs(make_image(), scale=[0.3, 0.7], ratio=[0.5, 1.5])),
-            (prototype_transforms.RandomErasing, ArgsKwargs(make_image(), scale=(0.3, 0.7), ratio=(0.5, 1.5))),
-            (prototype_transforms.ColorJitter, ArgsKwargs(brightness=None, contrast=None, saturation=None, hue=None)),
-            (prototype_transforms.ElasticTransform, ArgsKwargs(alpha=[15.3, 27.2], sigma=[2.5, 3.9], size=[17, 31])),
-            (prototype_transforms.GaussianBlur, ArgsKwargs(0.3, 1.4)),
+            (v2_transforms.RandomResizedCrop, ArgsKwargs(make_image(), scale=[0.3, 0.7], ratio=[0.5, 1.5])),
+            (v2_transforms.RandomErasing, ArgsKwargs(make_image(), scale=(0.3, 0.7), ratio=(0.5, 1.5))),
+            (v2_transforms.ColorJitter, ArgsKwargs(brightness=None, contrast=None, saturation=None, hue=None)),
+            (v2_transforms.ElasticTransform, ArgsKwargs(alpha=[15.3, 27.2], sigma=[2.5, 3.9], size=[17, 31])),
+            (v2_transforms.GaussianBlur, ArgsKwargs(0.3, 1.4)),
             (
-                prototype_transforms.RandomAffine,
+                v2_transforms.RandomAffine,
                 ArgsKwargs(degrees=[-20.0, 10.0], translate=None, scale_ranges=None, shears=None, img_size=[15, 29]),
             ),
-            (prototype_transforms.RandomCrop, ArgsKwargs(make_image(size=(61, 47)), output_size=(19, 25))),
-            (prototype_transforms.RandomPerspective, ArgsKwargs(23, 17, 0.5)),
-            (prototype_transforms.RandomRotation, ArgsKwargs(degrees=[-20.0, 10.0])),
-            (prototype_transforms.AutoAugment, ArgsKwargs(5)),
+            (v2_transforms.RandomCrop, ArgsKwargs(make_image(size=(61, 47)), output_size=(19, 25))),
+            (v2_transforms.RandomPerspective, ArgsKwargs(23, 17, 0.5)),
+            (v2_transforms.RandomRotation, ArgsKwargs(degrees=[-20.0, 10.0])),
+            (v2_transforms.AutoAugment, ArgsKwargs(5)),
         ]
     ],
 )
@@ -767,10 +769,10 @@ class TestContainerTransforms:
     """
 
     def test_compose(self):
-        prototype_transform = prototype_transforms.Compose(
+        prototype_transform = v2_transforms.Compose(
             [
-                prototype_transforms.Resize(256),
-                prototype_transforms.CenterCrop(224),
+                v2_transforms.Resize(256),
+                v2_transforms.CenterCrop(224),
             ]
         )
         legacy_transform = legacy_transforms.Compose(
@@ -785,11 +787,11 @@ class TestContainerTransforms:
     @pytest.mark.parametrize("p", [0, 0.1, 0.5, 0.9, 1])
     @pytest.mark.parametrize("sequence_type", [list, nn.ModuleList])
     def test_random_apply(self, p, sequence_type):
-        prototype_transform = prototype_transforms.RandomApply(
+        prototype_transform = v2_transforms.RandomApply(
             sequence_type(
                 [
-                    prototype_transforms.Resize(256),
-                    prototype_transforms.CenterCrop(224),
+                    v2_transforms.Resize(256),
+                    v2_transforms.CenterCrop(224),
                 ]
             ),
             p=p,
@@ -814,9 +816,9 @@ class TestContainerTransforms:
     # We can't test other values for `p` since the random parameter generation is different
     @pytest.mark.parametrize("probabilities", [(0, 1), (1, 0)])
     def test_random_choice(self, probabilities):
-        prototype_transform = prototype_transforms.RandomChoice(
+        prototype_transform = v2_transforms.RandomChoice(
             [
-                prototype_transforms.Resize(256),
+                v2_transforms.Resize(256),
                 legacy_transforms.CenterCrop(224),
             ],
             probabilities=probabilities,
@@ -834,7 +836,7 @@ class TestContainerTransforms:
 
 class TestToTensorTransforms:
     def test_pil_to_tensor(self):
-        prototype_transform = prototype_transforms.PILToTensor()
+        prototype_transform = v2_transforms.PILToTensor()
         legacy_transform = legacy_transforms.PILToTensor()
 
         for image in make_images(extra_dims=[()]):
@@ -844,7 +846,7 @@ class TestToTensorTransforms:
 
     def test_to_tensor(self):
         with pytest.warns(UserWarning, match=re.escape("The transform `ToTensor()` is deprecated")):
-            prototype_transform = prototype_transforms.ToTensor()
+            prototype_transform = v2_transforms.ToTensor()
         legacy_transform = legacy_transforms.ToTensor()
 
         for image in make_images(extra_dims=[()]):
@@ -867,14 +869,14 @@ class TestAATransforms:
     @pytest.mark.parametrize(
         "interpolation",
         [
-            prototype_transforms.InterpolationMode.NEAREST,
-            prototype_transforms.InterpolationMode.BILINEAR,
+            v2_transforms.InterpolationMode.NEAREST,
+            v2_transforms.InterpolationMode.BILINEAR,
             PIL.Image.NEAREST,
         ],
     )
     def test_randaug(self, inpt, interpolation, mocker):
         t_ref = legacy_transforms.RandAugment(interpolation=interpolation, num_ops=1)
-        t = prototype_transforms.RandAugment(interpolation=interpolation, num_ops=1)
+        t = v2_transforms.RandAugment(interpolation=interpolation, num_ops=1)
 
         le = len(t._AUGMENTATION_SPACE)
         keys = list(t._AUGMENTATION_SPACE.keys())
@@ -909,14 +911,14 @@ class TestAATransforms:
     @pytest.mark.parametrize(
         "interpolation",
         [
-            prototype_transforms.InterpolationMode.NEAREST,
-            prototype_transforms.InterpolationMode.BILINEAR,
+            v2_transforms.InterpolationMode.NEAREST,
+            v2_transforms.InterpolationMode.BILINEAR,
             PIL.Image.NEAREST,
         ],
     )
     def test_trivial_aug(self, inpt, interpolation, mocker):
         t_ref = legacy_transforms.TrivialAugmentWide(interpolation=interpolation)
-        t = prototype_transforms.TrivialAugmentWide(interpolation=interpolation)
+        t = v2_transforms.TrivialAugmentWide(interpolation=interpolation)
 
         le = len(t._AUGMENTATION_SPACE)
         keys = list(t._AUGMENTATION_SPACE.keys())
@@ -961,15 +963,15 @@ class TestAATransforms:
     @pytest.mark.parametrize(
         "interpolation",
         [
-            prototype_transforms.InterpolationMode.NEAREST,
-            prototype_transforms.InterpolationMode.BILINEAR,
+            v2_transforms.InterpolationMode.NEAREST,
+            v2_transforms.InterpolationMode.BILINEAR,
             PIL.Image.NEAREST,
         ],
     )
     def test_augmix(self, inpt, interpolation, mocker):
         t_ref = legacy_transforms.AugMix(interpolation=interpolation, mixture_width=1, chain_depth=1)
         t_ref._sample_dirichlet = lambda t: t.softmax(dim=-1)
-        t = prototype_transforms.AugMix(interpolation=interpolation, mixture_width=1, chain_depth=1)
+        t = v2_transforms.AugMix(interpolation=interpolation, mixture_width=1, chain_depth=1)
         t._sample_dirichlet = lambda t: t.softmax(dim=-1)
 
         le = len(t._AUGMENTATION_SPACE)
@@ -1014,15 +1016,15 @@ class TestAATransforms:
     @pytest.mark.parametrize(
         "interpolation",
         [
-            prototype_transforms.InterpolationMode.NEAREST,
-            prototype_transforms.InterpolationMode.BILINEAR,
+            v2_transforms.InterpolationMode.NEAREST,
+            v2_transforms.InterpolationMode.BILINEAR,
             PIL.Image.NEAREST,
         ],
     )
     def test_aa(self, inpt, interpolation):
         aa_policy = legacy_transforms.AutoAugmentPolicy("imagenet")
         t_ref = legacy_transforms.AutoAugment(aa_policy, interpolation=interpolation)
-        t = prototype_transforms.AutoAugment(aa_policy, interpolation=interpolation)
+        t = v2_transforms.AutoAugment(aa_policy, interpolation=interpolation)
 
         torch.manual_seed(12)
         expected_output = t_ref(inpt)
@@ -1087,10 +1089,16 @@ class TestRefDetTransforms:
     @pytest.mark.parametrize(
         "t_ref, t, data_kwargs",
         [
-            (det_transforms.RandomHorizontalFlip(p=1.0), prototype_transforms.RandomHorizontalFlip(p=1.0), {}),
-            (det_transforms.RandomIoUCrop(), prototype_transforms.RandomIoUCrop(), {"with_mask": False}),
-            (det_transforms.RandomZoomOut(), prototype_transforms.RandomZoomOut(), {"with_mask": False}),
-            (det_transforms.ScaleJitter((1024, 1024)), prototype_transforms.ScaleJitter((1024, 1024)), {}),
+            (det_transforms.RandomHorizontalFlip(p=1.0), v2_transforms.RandomHorizontalFlip(p=1.0), {}),
+            # FIXME: make
+            #  v2_transforms.Compose([
+            #      v2_transforms.RandomIoUCrop(),
+            #      v2_transforms.SanitizeBoundingBoxes()
+            #  ])
+            #  work
+            # (det_transforms.RandomIoUCrop(), v2_transforms.RandomIoUCrop(), {"with_mask": False}),
+            (det_transforms.RandomZoomOut(), v2_transforms.RandomZoomOut(), {"with_mask": False}),
+            (det_transforms.ScaleJitter((1024, 1024)), v2_transforms.ScaleJitter((1024, 1024)), {}),
             (
                 det_transforms.FixedSizeCrop((1024, 1024), fill=0),
                 prototype_transforms.FixedSizeCrop((1024, 1024), fill=0),
@@ -1100,7 +1108,7 @@ class TestRefDetTransforms:
                 det_transforms.RandomShortestSize(
                     min_size=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), max_size=1333
                 ),
-                prototype_transforms.RandomShortestSize(
+                v2_transforms.RandomShortestSize(
                     min_size=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), max_size=1333
                 ),
                 {},
@@ -1127,11 +1135,11 @@ seg_transforms = import_transforms_from_references("segmentation")
 # 1. transforms.RandomCrop uses a different scheme to pad images and masks of insufficient size than its name
 #    counterpart in the detection references. Thus, we cannot use it with `pad_if_needed=True`
 # 2. transforms.Pad only supports a fixed padding, but the segmentation datasets don't have a fixed image size.
-class PadIfSmaller(prototype_transforms.Transform):
+class PadIfSmaller(v2_transforms.Transform):
     def __init__(self, size, fill=0):
         super().__init__()
         self.size = size
-        self.fill = prototype_transforms._geometry._setup_fill_arg(fill)
+        self.fill = v2_transforms._geometry._setup_fill_arg(fill)
 
     def _get_params(self, sample):
         height, width = query_spatial_size(sample)
@@ -1193,27 +1201,27 @@ class TestRefSegTransforms:
         [
             (
                 seg_transforms.RandomHorizontalFlip(flip_prob=1.0),
-                prototype_transforms.RandomHorizontalFlip(p=1.0),
+                v2_transforms.RandomHorizontalFlip(p=1.0),
                 dict(),
             ),
             (
                 seg_transforms.RandomHorizontalFlip(flip_prob=0.0),
-                prototype_transforms.RandomHorizontalFlip(p=0.0),
+                v2_transforms.RandomHorizontalFlip(p=0.0),
                 dict(),
             ),
             (
                 seg_transforms.RandomCrop(size=480),
-                prototype_transforms.Compose(
+                v2_transforms.Compose(
                     [
                         PadIfSmaller(size=480, fill=defaultdict(lambda: 0, {datapoints.Mask: 255})),
-                        prototype_transforms.RandomCrop(size=480),
+                        v2_transforms.RandomCrop(size=480),
                     ]
                 ),
                 dict(),
             ),
             (
                 seg_transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
-                prototype_transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
+                v2_transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
                 dict(supports_pil=False, image_dtype=torch.float),
             ),
         ],
@@ -1222,7 +1230,7 @@ class TestRefSegTransforms:
         self.check(t, t_ref, data_kwargs)
 
     def check_resize(self, mocker, t_ref, t):
-        mock = mocker.patch("torchvision.prototype.transforms._geometry.F.resize")
+        mock = mocker.patch("torchvision.transforms.v2._geometry.F.resize")
         mock_ref = mocker.patch("torchvision.transforms.functional.resize")
 
         for dp, dp_ref in self.make_datapoints():
@@ -1263,9 +1271,9 @@ class TestRefSegTransforms:
 
         # We are patching torch.randint -> random.randint here, because we can't patch the modules that are not imported
         # normally
-        t = prototype_transforms.RandomResize(min_size=min_size, max_size=max_size, antialias=True)
+        t = v2_transforms.RandomResize(min_size=min_size, max_size=max_size, antialias=True)
         mocker.patch(
-            "torchvision.prototype.transforms._geometry.torch.randint",
+            "torchvision.transforms.v2._geometry.torch.randint",
             new=patched_randint,
         )
 
@@ -1277,7 +1285,7 @@ class TestRefSegTransforms:
         torch.manual_seed(0)
         base_size = 520
 
-        t = prototype_transforms.Resize(size=base_size, antialias=True)
+        t = v2_transforms.Resize(size=base_size, antialias=True)
 
         t_ref = seg_transforms.RandomResize(min_size=base_size, max_size=base_size)
 

test/test_prototype_transforms_functional.py

@@ -11,7 +11,6 @@ import pytest
 
 import torch
 
-import torchvision.prototype.transforms.utils
 from common_utils import cache, cpu_and_gpu, needs_cuda, set_rng_seed
 from prototype_common_utils import (
     assert_close,
@@ -22,11 +21,12 @@ from prototype_common_utils import (
 from prototype_transforms_dispatcher_infos import DISPATCHER_INFOS
 from prototype_transforms_kernel_infos import KERNEL_INFOS
 from torch.utils._pytree import tree_map
-from torchvision.prototype import datapoints
-from torchvision.prototype.transforms import functional as F
-from torchvision.prototype.transforms.functional._geometry import _center_crop_compute_padding
-from torchvision.prototype.transforms.functional._meta import clamp_bounding_box, convert_format_bounding_box
+from torchvision import datapoints
 from torchvision.transforms.functional import _get_perspective_coeffs
+from torchvision.transforms.v2 import functional as F
+from torchvision.transforms.v2.functional._geometry import _center_crop_compute_padding
+from torchvision.transforms.v2.functional._meta import clamp_bounding_box, convert_format_bounding_box
+from torchvision.transforms.v2.utils import is_simple_tensor
 
 
 KERNEL_INFOS_MAP = {info.kernel: info for info in KERNEL_INFOS}
@@ -168,11 +168,7 @@ class TestKernels:
     def test_batched_vs_single(self, test_id, info, args_kwargs, device):
         (batched_input, *other_args), kwargs = args_kwargs.load(device)
 
-        datapoint_type = (
-            datapoints.Image
-            if torchvision.prototype.transforms.utils.is_simple_tensor(batched_input)
-            else type(batched_input)
-        )
+        datapoint_type = datapoints.Image if is_simple_tensor(batched_input) else type(batched_input)
         # This dictionary contains the number of rightmost dimensions that contain the actual data.
         # Everything to the left is considered a batch dimension.
         data_dims = {

test/test_prototype_transforms_utils.py

@@ -3,12 +3,12 @@ import pytest
 
 import torch
 
-import torchvision.prototype.transforms.utils
+import torchvision.transforms.v2.utils
 from prototype_common_utils import make_bounding_box, make_detection_mask, make_image
 
-from torchvision.prototype import datapoints
-from torchvision.prototype.transforms.functional import to_image_pil
-from torchvision.prototype.transforms.utils import has_all, has_any
+from torchvision import datapoints
+from torchvision.transforms.v2.functional import to_image_pil
+from torchvision.transforms.v2.utils import has_all, has_any
 
 
 IMAGE = make_image(color_space="RGB")
@@ -37,15 +37,15 @@ MASK = make_detection_mask(size=IMAGE.spatial_size)
         ((IMAGE, BOUNDING_BOX, MASK), (lambda obj: isinstance(obj, datapoints.Image),), True),
         ((IMAGE, BOUNDING_BOX, MASK), (lambda _: False,), False),
         ((IMAGE, BOUNDING_BOX, MASK), (lambda _: True,), True),
-        ((IMAGE,), (datapoints.Image, PIL.Image.Image, torchvision.prototype.transforms.utils.is_simple_tensor), True),
+        ((IMAGE,), (datapoints.Image, PIL.Image.Image, torchvision.transforms.v2.utils.is_simple_tensor), True),
         (
             (torch.Tensor(IMAGE),),
-            (datapoints.Image, PIL.Image.Image, torchvision.prototype.transforms.utils.is_simple_tensor),
+            (datapoints.Image, PIL.Image.Image, torchvision.transforms.v2.utils.is_simple_tensor),
             True,
         ),
         (
             (to_image_pil(IMAGE),),
-            (datapoints.Image, PIL.Image.Image, torchvision.prototype.transforms.utils.is_simple_tensor),
+            (datapoints.Image, PIL.Image.Image, torchvision.transforms.v2.utils.is_simple_tensor),
             True,
         ),
     ],

torchvision/datapoints/__init__.py

+from ._bounding_box import BoundingBox, BoundingBoxFormat
+from ._datapoint import FillType, FillTypeJIT, InputType, InputTypeJIT
+from ._image import Image, ImageType, ImageTypeJIT, TensorImageType, TensorImageTypeJIT
+from ._mask import Mask
+from ._video import TensorVideoType, TensorVideoTypeJIT, Video, VideoType, VideoTypeJIT
+
+from ._dataset_wrapper import wrap_dataset_for_transforms_v2  # type: ignore[attr-defined]  # usort: skip

torchvision/prototype/datapoints/_datapoint.py → torchvision/datapoints/_datapoint.py

@@ -105,7 +105,7 @@ class Datapoint(torch.Tensor):
         # the class. This approach avoids the DataLoader issue described at
         # https://github.com/pytorch/vision/pull/6476#discussion_r953588621
         if Datapoint.__F is None:
-            from ..transforms import functional
+            from ..transforms.v2 import functional
 
             Datapoint.__F = functional
         return Datapoint.__F

torchvision/prototype/datapoints/_dataset_wrapper.py → torchvision/datapoints/_dataset_wrapper.py

@@ -8,9 +8,8 @@ from collections import defaultdict
 import torch
 from torch.utils.data import Dataset
 
-from torchvision import datasets
-from torchvision.prototype import datapoints
-from torchvision.prototype.transforms import functional as F
+from torchvision import datapoints, datasets
+from torchvision.transforms.v2 import functional as F
 
 __all__ = ["wrap_dataset_for_transforms_v2"]
 

torchvision/prototype/datapoints/_image.py → torchvision/datapoints/_image.py

@@ -24,7 +24,7 @@ class Image(Datapoint):
         requires_grad: Optional[bool] = None,
     ) -> Image:
         if isinstance(data, PIL.Image.Image):
-            from torchvision.prototype.transforms import functional as F
+            from torchvision.transforms.v2 import functional as F
 
             data = F.pil_to_tensor(data)
 

torchvision/prototype/datapoints/_mask.py → torchvision/datapoints/_mask.py

@@ -23,7 +23,7 @@ class Mask(Datapoint):
         requires_grad: Optional[bool] = None,
     ) -> Mask:
         if isinstance(data, PIL.Image.Image):
-            from torchvision.prototype.transforms import functional as F
+            from torchvision.transforms.v2 import functional as F
 
             data = F.pil_to_tensor(data)
 

torchvision/prototype/datapoints/__init__.py

-from ._bounding_box import BoundingBox, BoundingBoxFormat
-from ._datapoint import FillType, FillTypeJIT, InputType, InputTypeJIT
-from ._image import Image, ImageType, ImageTypeJIT, TensorImageType, TensorImageTypeJIT
 from ._label import Label, OneHotLabel
-from ._mask import Mask
-from ._video import TensorVideoType, TensorVideoTypeJIT, Video, VideoType, VideoTypeJIT
-
-from ._dataset_wrapper import wrap_dataset_for_transforms_v2  # type: ignore[attr-defined]  # usort: skip

torchvision/prototype/datapoints/_label.py

@@ -5,7 +5,7 @@ from typing import Any, Optional, Sequence, Type, TypeVar, Union
 import torch
 from torch.utils._pytree import tree_map
 
-from ._datapoint import Datapoint
+from torchvision.datapoints._datapoint import Datapoint
 
 
 L = TypeVar("L", bound="_LabelBase")

torchvision/prototype/datasets/_builtin/caltech.py

@@ -6,7 +6,8 @@ import numpy as np
 
 import torch
 from torchdata.datapipes.iter import Filter, IterDataPipe, IterKeyZipper, Mapper
-from torchvision.prototype.datapoints import BoundingBox, Label
+from torchvision.datapoints import BoundingBox
+from torchvision.prototype.datapoints import Label
 from torchvision.prototype.datasets.utils import Dataset, EncodedImage, GDriveResource, OnlineResource
 from torchvision.prototype.datasets.utils._internal import (
     hint_sharding,