Change betastatus doc warning and v2 import warning (#7329)

docs/source/beta_status.py

@@ -4,15 +4,26 @@ from docutils.parsers.rst import Directive
 
 class BetaStatus(Directive):
     has_content = True
+    text = "The {api_name} is in Beta stage, and backward compatibility is not guaranteed."
 
     def run(self):
-        api_name = " ".join(self.content)
-        text = f"The {api_name} is in Beta stage, and backward compatibility is not guaranteed."
+        text = self.text.format(api_name=" ".join(self.content))
         return [nodes.warning("", nodes.paragraph("", "", nodes.Text(text)))]
 
 
+class V2BetaStatus(BetaStatus):
+    text = (
+        "The {api_name} is in Beta stage, and while we do not expect major breaking changes, "
+        "some APIs may still change according to user feedback. Please submit any feedback you may have "
+        "in this issue: https://github.com/pytorch/vision/issues/6753, and you can also check "
+        "out https://github.com/pytorch/vision/issues/7319 to learn "
+        "more about the APIs that we suspect might involve future changes."
+    )
+
+
 def setup(app):
     app.add_directive("betastatus", BetaStatus)
+    app.add_directive("v2betastatus", V2BetaStatus)
     return {
         "version": "0.1",
         "parallel_read_safe": True,

docs/source/transforms.rst

@@ -16,10 +16,10 @@ Transforming and augmenting images
     :ref:`sphx_glr_auto_examples_plot_transforms_v2_e2e.py`.
     Note that these transforms are still BETA, and while we don't expect major
     breaking changes in the future, some APIs may still change according to user
-    feedback. Please submit any feedback you may have in
-    https://github.com/pytorch/vision/issues/6753, and you can also check out
-    https://github.com/pytorch/vision/issues/7319 to learn more about the APIs
-    that we suspect might involve future changes.
+    feedback. Please submit any feedback you may have `here
+    <https://github.com/pytorch/vision/issues/6753>`_, and you can also check
+    out `this issue <https://github.com/pytorch/vision/issues/7319>`_ to learn
+    more about the APIs that we suspect might involve future changes.
 
 Transforms are common image transformations available in the
 ``torchvision.transforms`` module. They can be chained together using

torchvision/__init__.py

@@ -100,10 +100,11 @@ def _is_tracing():
 _WARN_ABOUT_BETA_TRANSFORMS = True
 _BETA_TRANSFORMS_WARNING = (
     "The torchvision.datapoints and torchvision.transforms.v2 namespaces are still Beta. "
-    "While we will try our best to maintain backward compatibility, "
-    "some APIs or behaviors might change without a deprecation cycle. "
-    "To help us improve these new features, please provide your feedback "
-    "here: https://github.com/pytorch/vision/issues/6753."
+    "While we do not expect major breaking changes, some APIs may still change "
+    "according to user feedback. Please submit any feedback you may have in "
+    "this issue: https://github.com/pytorch/vision/issues/6753, and you can also "
+    "check out https://github.com/pytorch/vision/issues/7319 to learn more about "
+    "the APIs that we suspect might involve future changes. "
     "You can silence this warning by calling torchvision.disable_beta_transform_warning()."
 )
 

torchvision/transforms/v2/_augment.py

@@ -15,7 +15,7 @@ from .utils import is_simple_tensor, query_chw
 class RandomErasing(_RandomApplyTransform):
     """[BETA] Randomly select a rectangle region in the input image or video and erase its pixels.
 
-    .. betastatus:: RandomErasing transform
+    .. v2betastatus:: RandomErasing transform
 
     This transform does not support PIL Image.
     'Random Erasing Data Augmentation' by Zhong et al. See https://arxiv.org/abs/1708.04896

torchvision/transforms/v2/_auto_augment.py

@@ -165,7 +165,7 @@ class AutoAugment(_AutoAugmentBase):
     r"""[BETA] AutoAugment data augmentation method based on
     `"AutoAugment: Learning Augmentation Strategies from Data" <https://arxiv.org/pdf/1805.09501.pdf>`_.
 
-    .. betastatus:: AutoAugment transform
+    .. v2betastatus:: AutoAugment transform
 
     This transformation works on images and videos only.
 
@@ -342,7 +342,7 @@ class RandAugment(_AutoAugmentBase):
     `"RandAugment: Practical automated data augmentation with a reduced search space"
     <https://arxiv.org/abs/1909.13719>`_.
 
-    .. betastatus:: RandAugment transform
+    .. v2betastatus:: RandAugment transform
 
     This transformation works on images and videos only.
 
@@ -425,7 +425,7 @@ class TrivialAugmentWide(_AutoAugmentBase):
     r"""[BETA] Dataset-independent data-augmentation with TrivialAugment Wide, as described in
     `"TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation" <https://arxiv.org/abs/2103.10158>`_.
 
-    .. betastatus:: TrivialAugmentWide transform
+    .. v2betastatus:: TrivialAugmentWide transform
 
     This transformation works on images and videos only.
 
@@ -496,7 +496,7 @@ class AugMix(_AutoAugmentBase):
     r"""[BETA] AugMix data augmentation method based on
     `"AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty" <https://arxiv.org/abs/1912.02781>`_.
 
-    .. betastatus:: AugMix transform
+    .. v2betastatus:: AugMix transform
 
     This transformation works on images and videos only.
 

torchvision/transforms/v2/_color.py

@@ -13,7 +13,7 @@ from .utils import is_simple_tensor, query_chw
 class Grayscale(Transform):
     """[BETA] Convert images or videos to grayscale.
 
-    .. betastatus:: Grayscale transform
+    .. v2betastatus:: Grayscale transform
 
     If the input is a :class:`torch.Tensor`, it is expected
     to have [..., 3 or 1, H, W] shape, where ... means an arbitrary number of leading dimensions
@@ -42,7 +42,7 @@ class Grayscale(Transform):
 class RandomGrayscale(_RandomApplyTransform):
     """[BETA] Randomly convert image or videos to grayscale with a probability of p (default 0.1).
 
-    .. betastatus:: RandomGrayscale transform
+    .. v2betastatus:: RandomGrayscale transform
 
     If the input is a :class:`torch.Tensor`, it is expected to have [..., 3 or 1, H, W] shape,
     where ... means an arbitrary number of leading dimensions
@@ -76,7 +76,7 @@ class RandomGrayscale(_RandomApplyTransform):
 class ColorJitter(Transform):
     """[BETA] Randomly change the brightness, contrast, saturation and hue of an image or video.
 
-    .. betastatus:: ColorJitter transform
+    .. v2betastatus:: ColorJitter transform
 
     If the input is a :class:`torch.Tensor`, it is expected
     to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
@@ -182,7 +182,7 @@ class RandomPhotometricDistort(Transform):
     """[BETA] Randomly distorts the image or video as used in `SSD: Single Shot
     MultiBox Detector <https://arxiv.org/abs/1512.02325>`_.
 
-    .. betastatus:: RandomPhotometricDistort transform
+    .. v2betastatus:: RandomPhotometricDistort transform
 
     This transform relies on :class:`~torchvision.transforms.v2.ColorJitter`
     under the hood to adjust the contrast, saturation, hue, brightness, and also
@@ -282,7 +282,7 @@ class RandomPhotometricDistort(Transform):
 class RandomEqualize(_RandomApplyTransform):
     """[BETA] Equalize the histogram of the given image or video with a given probability.
 
-    .. betastatus:: RandomEqualize transform
+    .. v2betastatus:: RandomEqualize transform
 
     If the input is a :class:`torch.Tensor`, it is expected
     to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
@@ -301,7 +301,7 @@ class RandomEqualize(_RandomApplyTransform):
 class RandomInvert(_RandomApplyTransform):
     """[BETA] Inverts the colors of the given image or video with a given probability.
 
-    .. betastatus:: RandomInvert transform
+    .. v2betastatus:: RandomInvert transform
 
     If img is a Tensor, it is expected to be in [..., 1 or 3, H, W] format,
     where ... means it can have an arbitrary number of leading dimensions.
@@ -321,7 +321,7 @@ class RandomPosterize(_RandomApplyTransform):
     """[BETA] Posterize the image or video with a given probability by reducing the
     number of bits for each color channel.
 
-    .. betastatus:: RandomPosterize transform
+    .. v2betastatus:: RandomPosterize transform
 
     If the input is a :class:`torch.Tensor`, it should be of type torch.uint8,
     and it is expected to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
@@ -346,7 +346,7 @@ class RandomSolarize(_RandomApplyTransform):
     """[BETA] Solarize the image or video with a given probability by inverting all pixel
     values above a threshold.
 
-    .. betastatus:: RandomSolarize transform
+    .. v2betastatus:: RandomSolarize transform
 
     If img is a Tensor, it is expected to be in [..., 1 or 3, H, W] format,
     where ... means it can have an arbitrary number of leading dimensions.
@@ -370,7 +370,7 @@ class RandomSolarize(_RandomApplyTransform):
 class RandomAutocontrast(_RandomApplyTransform):
     """[BETA] Autocontrast the pixels of the given image or video with a given probability.
 
-    .. betastatus:: RandomAutocontrast transform
+    .. v2betastatus:: RandomAutocontrast transform
 
     If the input is a :class:`torch.Tensor`, it is expected
     to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
@@ -389,7 +389,7 @@ class RandomAutocontrast(_RandomApplyTransform):
 class RandomAdjustSharpness(_RandomApplyTransform):
     """[BETA] Adjust the sharpness of the image or video with a given probability.
 
-    .. betastatus:: RandomAdjustSharpness transform
+    .. v2betastatus:: RandomAdjustSharpness transform
 
     If the input is a :class:`torch.Tensor`,
     it is expected to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.

torchvision/transforms/v2/_container.py

@@ -10,7 +10,7 @@ from torchvision.transforms.v2 import Transform
 class Compose(Transform):
     """[BETA] Composes several transforms together.
 
-    .. betastatus:: Compose transform
+    .. v2betastatus:: Compose transform
 
     This transform does not support torchscript.
     Please, see the note below.
@@ -61,7 +61,7 @@ class Compose(Transform):
 class RandomApply(Transform):
     """[BETA] Apply randomly a list of transformations with a given probability.
 
-    .. betastatus:: RandomApply transform
+    .. v2betastatus:: RandomApply transform
 
     .. note::
         In order to script the transformation, please use ``torch.nn.ModuleList`` as input instead of list/tuple of
@@ -116,7 +116,7 @@ class RandomApply(Transform):
 class RandomChoice(Transform):
     """[BETA] Apply single transformation randomly picked from a list.
 
-    .. betastatus:: RandomChoice transform
+    .. v2betastatus:: RandomChoice transform
 
     This transform does not support torchscript.
 
@@ -155,7 +155,7 @@ class RandomChoice(Transform):
 class RandomOrder(Transform):
     """[BETA] Apply a list of transformations in a random order.
 
-    .. betastatus:: RandomOrder transform
+    .. v2betastatus:: RandomOrder transform
 
     This transform does not support torchscript.
 

torchvision/transforms/v2/_deprecated.py

@@ -12,7 +12,7 @@ from torchvision.transforms.v2 import Transform
 class ToTensor(Transform):
     """[BETA] Convert a PIL Image or ndarray to tensor and scale the values accordingly.
 
-    .. betastatus:: ToTensor transform
+    .. v2betastatus:: ToTensor transform
 
     .. warning::
         :class:`v2.ToTensor` is deprecated and will be removed in a future release.

torchvision/transforms/v2/_geometry.py

@@ -28,7 +28,7 @@ from .utils import has_all, has_any, is_simple_tensor, query_bounding_box, query
 class RandomHorizontalFlip(_RandomApplyTransform):
     """[BETA] Horizontally flip the input with a given probability.
 
-    .. betastatus:: RandomHorizontalFlip transform
+    .. v2betastatus:: RandomHorizontalFlip transform
 
     If the input is a :class:`torch.Tensor` or a ``Datapoint`` (e.g. :class:`~torchvision.datapoints.Image`,
     :class:`~torchvision.datapoints.Video`, :class:`~torchvision.datapoints.BoundingBox` etc.)
@@ -48,7 +48,7 @@ class RandomHorizontalFlip(_RandomApplyTransform):
 class RandomVerticalFlip(_RandomApplyTransform):
     """[BETA] Vertically flip the input with a given probability.
 
-    .. betastatus:: RandomVerticalFlip transform
+    .. v2betastatus:: RandomVerticalFlip transform
 
     If the input is a :class:`torch.Tensor` or a ``Datapoint`` (e.g. :class:`~torchvision.datapoints.Image`,
     :class:`~torchvision.datapoints.Video`, :class:`~torchvision.datapoints.BoundingBox` etc.)
@@ -68,7 +68,7 @@ class RandomVerticalFlip(_RandomApplyTransform):
 class Resize(Transform):
     """[BETA] Resize the input to the given size.
 
-    .. betastatus:: Resize transform
+    .. v2betastatus:: Resize transform
 
     If the input is a :class:`torch.Tensor` or a ``Datapoint`` (e.g. :class:`~torchvision.datapoints.Image`,
     :class:`~torchvision.datapoints.Video`, :class:`~torchvision.datapoints.BoundingBox` etc.)
@@ -162,7 +162,7 @@ class Resize(Transform):
 class CenterCrop(Transform):
     """[BETA] Crop the input at the center.
 
-    .. betastatus:: CenterCrop transform
+    .. v2betastatus:: CenterCrop transform
 
     If the input is a :class:`torch.Tensor` or a ``Datapoint`` (e.g. :class:`~torchvision.datapoints.Image`,
     :class:`~torchvision.datapoints.Video`, :class:`~torchvision.datapoints.BoundingBox` etc.)
@@ -190,7 +190,7 @@ class CenterCrop(Transform):
 class RandomResizedCrop(Transform):
     """[BETA] Crop a random portion of the input and resize it to a given size.
 
-    .. betastatus:: RandomResizedCrop transform
+    .. v2betastatus:: RandomResizedCrop transform
 
     If the input is a :class:`torch.Tensor` or a ``Datapoint`` (e.g. :class:`~torchvision.datapoints.Image`,
     :class:`~torchvision.datapoints.Video`, :class:`~torchvision.datapoints.BoundingBox` etc.)
@@ -316,7 +316,7 @@ ImageOrVideoTypeJIT = Union[datapoints._ImageTypeJIT, datapoints._VideoTypeJIT]
 class FiveCrop(Transform):
     """[BETA] Crop the image or video into four corners and the central crop.
 
-    .. betastatus:: FiveCrop transform
+    .. v2betastatus:: FiveCrop transform
 
     If the input is a :class:`torch.Tensor` or a :class:`~torchvision.datapoints.Image` or a
     :class:`~torchvision.datapoints.Video` it can have arbitrary number of leading batch dimensions.
@@ -379,7 +379,7 @@ class TenCrop(Transform):
     """[BETA] Crop the image or video into four corners and the central crop plus the flipped version of
     these (horizontal flipping is used by default).
 
-    .. betastatus:: TenCrop transform
+    .. v2betastatus:: TenCrop transform
 
     If the input is a :class:`torch.Tensor` or a :class:`~torchvision.datapoints.Image` or a
     :class:`~torchvision.datapoints.Video` it can have arbitrary number of leading batch dimensions.
@@ -437,7 +437,7 @@ class TenCrop(Transform):
 class Pad(Transform):
     """[BETA] Pad the input on all sides with the given "pad" value.
 
-    .. betastatus:: Pad transform
+    .. v2betastatus:: Pad transform
 
     If the input is a :class:`torch.Tensor` or a ``Datapoint`` (e.g. :class:`~torchvision.datapoints.Image`,
     :class:`~torchvision.datapoints.Video`, :class:`~torchvision.datapoints.BoundingBox` etc.)
@@ -512,7 +512,7 @@ class RandomZoomOut(_RandomApplyTransform):
     """[BETA] "Zoom out" transformation from
     `"SSD: Single Shot MultiBox Detector" <https://arxiv.org/abs/1512.02325>`_.
 
-    .. betastatus:: RandomZoomOut transform
+    .. v2betastatus:: RandomZoomOut transform
 
     This transformation randomly pads images, videos, bounding boxes and masks creating a zoom out effect.
     Output spatial size is randomly sampled from original size up to a maximum size configured
@@ -581,7 +581,7 @@ class RandomZoomOut(_RandomApplyTransform):
 class RandomRotation(Transform):
     """[BETA] Rotate the input by angle.
 
-    .. betastatus:: RandomRotation transform
+    .. v2betastatus:: RandomRotation transform
 
     If the input is a :class:`torch.Tensor` or a ``Datapoint`` (e.g. :class:`~torchvision.datapoints.Image`,
     :class:`~torchvision.datapoints.Video`, :class:`~torchvision.datapoints.BoundingBox` etc.)
@@ -654,7 +654,7 @@ class RandomRotation(Transform):
 class RandomAffine(Transform):
     """[BETA] Random affine transformation the input keeping center invariant.
 
-    .. betastatus:: RandomAffine transform
+    .. v2betastatus:: RandomAffine transform
 
     If the input is a :class:`torch.Tensor` or a ``Datapoint`` (e.g. :class:`~torchvision.datapoints.Image`,
     :class:`~torchvision.datapoints.Video`, :class:`~torchvision.datapoints.BoundingBox` etc.)
@@ -775,7 +775,7 @@ class RandomAffine(Transform):
 class RandomCrop(Transform):
     """[BETA] Crop the input at a random location.
 
-    .. betastatus:: RandomCrop transform
+    .. v2betastatus:: RandomCrop transform
 
     If the input is a :class:`torch.Tensor` or a ``Datapoint`` (e.g. :class:`~torchvision.datapoints.Image`,
     :class:`~torchvision.datapoints.Video`, :class:`~torchvision.datapoints.BoundingBox` etc.)
@@ -930,7 +930,7 @@ class RandomCrop(Transform):
 class RandomPerspective(_RandomApplyTransform):
     """[BETA] Perform a random perspective transformation of the input with a given probability.
 
-    .. betastatus:: RandomPerspective transform
+    .. v2betastatus:: RandomPerspective transform
 
     If the input is a :class:`torch.Tensor` or a ``Datapoint`` (e.g. :class:`~torchvision.datapoints.Image`,
     :class:`~torchvision.datapoints.Video`, :class:`~torchvision.datapoints.BoundingBox` etc.)
@@ -1016,7 +1016,7 @@ class RandomPerspective(_RandomApplyTransform):
 class ElasticTransform(Transform):
     """[BETA] Transform the input with elastic transformations.
 
-    .. betastatus:: RandomPerspective transform
+    .. v2betastatus:: RandomPerspective transform
 
     If the input is a :class:`torch.Tensor` or a ``Datapoint`` (e.g. :class:`~torchvision.datapoints.Image`,
     :class:`~torchvision.datapoints.Video`, :class:`~torchvision.datapoints.BoundingBox` etc.)
@@ -1108,7 +1108,7 @@ class RandomIoUCrop(Transform):
     """[BETA] Random IoU crop transformation from
     `"SSD: Single Shot MultiBox Detector" <https://arxiv.org/abs/1512.02325>`_.
 
-    .. betastatus:: RandomIoUCrop transform
+    .. v2betastatus:: RandomIoUCrop transform
 
     This transformation requires an image or video data and ``datapoints.BoundingBox`` in the input.
 
@@ -1232,7 +1232,7 @@ class ScaleJitter(Transform):
     """[BETA] Perform Large Scale Jitter on the input according to
     `"Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation" <https://arxiv.org/abs/2012.07177>`_.
 
-    .. betastatus:: ScaleJitter transform
+    .. v2betastatus:: ScaleJitter transform
 
     If the input is a :class:`torch.Tensor` or a ``Datapoint`` (e.g. :class:`~torchvision.datapoints.Image`,
     :class:`~torchvision.datapoints.Video`, :class:`~torchvision.datapoints.BoundingBox` etc.)
@@ -1298,7 +1298,7 @@ class ScaleJitter(Transform):
 class RandomShortestSize(Transform):
     """[BETA] Randomly resize the input.
 
-    .. betastatus:: RandomShortestSize transform
+    .. v2betastatus:: RandomShortestSize transform
 
     If the input is a :class:`torch.Tensor` or a ``Datapoint`` (e.g. :class:`~torchvision.datapoints.Image`,
     :class:`~torchvision.datapoints.Video`, :class:`~torchvision.datapoints.BoundingBox` etc.)
@@ -1366,7 +1366,7 @@ class RandomShortestSize(Transform):
 class RandomResize(Transform):
     """[BETA] Randomly resize the input.
 
-    .. betastatus:: RandomResize transform
+    .. v2betastatus:: RandomResize transform
 
     This transformation can be used together with ``RandomCrop`` as data augmentations to train
     models on image segmentation task.

torchvision/transforms/v2/_meta.py

@@ -11,7 +11,7 @@ from .utils import is_simple_tensor
 class ConvertBoundingBoxFormat(Transform):
     """[BETA] Convert bounding box coordinates to the given ``format``, eg from "CXCYWH" to "XYXY".
 
-    .. betastatus:: ConvertBoundingBoxFormat transform
+    .. v2betastatus:: ConvertBoundingBoxFormat transform
 
     Args:
         format (str or datapoints.BoundingBoxFormat): output bounding box format.
@@ -34,7 +34,7 @@ class ConvertBoundingBoxFormat(Transform):
 class ConvertDtype(Transform):
     """[BETA] Convert input image or video to the given ``dtype`` and scale the values accordingly.
 
-    .. betastatus:: ConvertDtype transform
+    .. v2betastatus:: ConvertDtype transform
 
     This function does not support PIL Image.
 
@@ -77,7 +77,7 @@ class ClampBoundingBox(Transform):
 
     The clamping is done according to the bounding boxes' ``spatial_size`` meta-data.
 
-    .. betastatus:: ClampBoundingBox transform
+    .. v2betastatus:: ClampBoundingBox transform
 
     """
 

torchvision/transforms/v2/_misc.py

@@ -24,7 +24,7 @@ class Identity(Transform):
 class Lambda(Transform):
     """[BETA] Apply a user-defined function as a transform.
 
-    .. betastatus:: Lambda transform
+    .. v2betastatus:: Lambda transform
 
     This transform does not support torchscript.
 
@@ -55,7 +55,7 @@ class Lambda(Transform):
 class LinearTransformation(Transform):
     """[BETA] Transform a tensor image or video with a square transformation matrix and a mean_vector computed offline.
 
-    .. betastatus:: LinearTransformation transform
+    .. v2betastatus:: LinearTransformation transform
 
     This transform does not support PIL Image.
     Given transformation_matrix and mean_vector, will flatten the torch.*Tensor and
@@ -138,7 +138,7 @@ class LinearTransformation(Transform):
 class Normalize(Transform):
     """[BETA] Normalize a tensor image or video with mean and standard deviation.
 
-    .. betastatus:: Normalize transform
+    .. v2betastatus:: Normalize transform
 
     This transform does not support PIL Image.
     Given mean: ``(mean[1],...,mean[n])`` and std: ``(std[1],..,std[n])`` for ``n``
@@ -178,7 +178,7 @@ class Normalize(Transform):
 class GaussianBlur(Transform):
     """[BETA] Blurs image with randomly chosen Gaussian blur.
 
-    .. betastatus:: GausssianBlur transform
+    .. v2betastatus:: GausssianBlur transform
 
     If the input is a Tensor, it is expected
     to have [..., C, H, W] shape, where ... means an arbitrary number of leading dimensions.
@@ -225,7 +225,7 @@ class GaussianBlur(Transform):
 class ToDtype(Transform):
     """[BETA] Converts the input to a specific dtype - this does not scale values.
 
-    .. betastatus:: ToDtype transform
+    .. v2betastatus:: ToDtype transform
 
     Args:
         dtype (``torch.dtype`` or dict of ``Datapoint`` -> ``torch.dtype``): The dtype to convert to.
@@ -258,7 +258,7 @@ class ToDtype(Transform):
 class SanitizeBoundingBox(Transform):
     """[BETA] Remove degenerate/invalid bounding boxes and their corresponding labels and masks.
 
-    .. betastatus:: SanitizeBoundingBox transform
+    .. v2betastatus:: SanitizeBoundingBox transform
 
     This transform removes bounding boxes and their associated labels/masks that:
 

torchvision/transforms/v2/_temporal.py

@@ -9,7 +9,7 @@ from torchvision.transforms.v2.utils import is_simple_tensor
 class UniformTemporalSubsample(Transform):
     """[BETA] Uniformly subsample ``num_samples`` indices from the temporal dimension of the video.
 
-    .. betastatus:: UniformTemporalSubsample transform
+    .. v2betastatus:: UniformTemporalSubsample transform
 
     Videos are expected to be of shape ``[..., T, C, H, W]`` where ``T`` denotes the temporal dimension.
 

torchvision/transforms/v2/_type_conversion.py

@@ -13,7 +13,7 @@ from torchvision.transforms.v2.utils import is_simple_tensor
 class PILToTensor(Transform):
     """[BETA] Convert a PIL Image to a tensor of the same type - this does not scale values.
 
-    .. betastatus:: PILToTensor transform
+    .. v2betastatus:: PILToTensor transform
 
     This transform does not support torchscript.
 
@@ -30,7 +30,7 @@ class ToImageTensor(Transform):
     """[BETA] Convert a tensor, ndarray, or PIL Image to :class:`~torchvision.datapoints.Image`
     ; this does not scale values.
 
-    .. betastatus:: ToImageTensor transform
+    .. v2betastatus:: ToImageTensor transform
 
     This transform does not support torchscript.
     """
@@ -46,7 +46,7 @@ class ToImageTensor(Transform):
 class ToImagePIL(Transform):
     """[BETA] Convert a tensor or an ndarray to PIL Image - this does not scale values.
 
-    .. betastatus:: ToImagePIL transform
+    .. v2betastatus:: ToImagePIL transform
 
     This transform does not support torchscript.