Remove BETA status for v2 transforms (#8111)

Author: NicolasHug

docs/source/beta_status.py

@@ -12,18 +12,8 @@ def run(self):
  return [self.node("", nodes.paragraph("", "", nodes.Text(text)))]
 
 
-class V2BetaStatus(BetaStatus):
- text = (
- "The {api_name} is in Beta stage, and while we do not expect disruptive breaking changes, "
- "some APIs may slightly change according to user feedback. Please submit any feedback you may have "
- "in this issue: https://github.com/pytorch/vision/issues/6753."
- )
- node = nodes.note
-
-
 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

@@ -126,13 +126,6 @@ you're already using tranforms from ``torchvision.transforms``, all you need to
 do to is to update the import to ``torchvision.transforms.v2``. In terms of
 output, there might be negligible differences due to implementation differences.
 
-.. note::
-
- The v2 transforms are still BETA, but at this point we do not expect
- disruptive changes to be made to their public APIs. We're planning to make
- them fully stable in version 0.17. Please submit any feedback you may have
- `here <https://github.com/pytorch/vision/issues/6753>`_.
-
 .. _transforms_perf:
 
 Performance considerations

torchvision/transforms/v2/_augment.py

@@ -15,9 +15,7 @@
 
 
 class RandomErasing(_RandomApplyTransform):
- """[BETA] Randomly select a rectangle region in the input image or video and erase its pixels.
-
- .. v2betastatus:: RandomErasing transform
+ """Randomly select a rectangle region in the input image or video and erase its pixels.
 
  This transform does not support PIL Image.
  'Random Erasing Data Augmentation' by Zhong et al. See https://arxiv.org/abs/1708.04896
@@ -207,9 +205,7 @@ def _mixup_label(self, label: torch.Tensor, *, lam: float) -> torch.Tensor:
 
 
 class MixUp(_BaseMixUpCutMix):
- """[BETA] Apply MixUp to the provided batch of images and labels.
-
- .. v2betastatus:: MixUp transform
+ """Apply MixUp to the provided batch of images and labels.
 
  Paper: `mixup: Beyond Empirical Risk Minimization <https://arxiv.org/abs/1710.09412>`_.
 
@@ -256,9 +252,7 @@ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
 
 
 class CutMix(_BaseMixUpCutMix):
- """[BETA] Apply CutMix to the provided batch of images and labels.
-
- .. v2betastatus:: CutMix transform
+ """Apply CutMix to the provided batch of images and labels.
 
  Paper: `CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
  <https://arxiv.org/abs/1905.04899>`_.

torchvision/transforms/v2/_auto_augment.py

@@ -174,11 +174,9 @@ def _apply_image_or_video_transform(
 
 
 class AutoAugment(_AutoAugmentBase):
- r"""[BETA] AutoAugment data augmentation method based on
+ r"""AutoAugment data augmentation method based on
  `"AutoAugment: Learning Augmentation Strategies from Data" <https://arxiv.org/pdf/1805.09501.pdf>`_.
 
- .. v2betastatus:: AutoAugment transform
-
  This transformation works on images and videos only.
 
  If the input is :class:`torch.Tensor`, it should be of type ``torch.uint8``, and it is expected
@@ -350,12 +348,10 @@ def forward(self, *inputs: Any) -> Any:
 
 
 class RandAugment(_AutoAugmentBase):
- r"""[BETA] RandAugment data augmentation method based on
+ r"""RandAugment data augmentation method based on
  `"RandAugment: Practical automated data augmentation with a reduced search space"
  <https://arxiv.org/abs/1909.13719>`_.
 
- .. v2betastatus:: RandAugment transform
-
  This transformation works on images and videos only.
 
  If the input is :class:`torch.Tensor`, it should be of type ``torch.uint8``, and it is expected
@@ -434,11 +430,9 @@ def forward(self, *inputs: Any) -> Any:
 
 
 class TrivialAugmentWide(_AutoAugmentBase):
- r"""[BETA] Dataset-independent data-augmentation with TrivialAugment Wide, as described in
+ r"""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>`_.
 
- .. v2betastatus:: TrivialAugmentWide transform
-
  This transformation works on images and videos only.
 
  If the input is :class:`torch.Tensor`, it should be of type ``torch.uint8``, and it is expected
@@ -505,11 +499,9 @@ def forward(self, *inputs: Any) -> Any:
 
 
 class AugMix(_AutoAugmentBase):
- r"""[BETA] AugMix data augmentation method based on
+ r"""AugMix data augmentation method based on
  `"AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty" <https://arxiv.org/abs/1912.02781>`_.
 
- .. v2betastatus:: AugMix transform
-
  This transformation works on images and videos only.
 
  If the input is :class:`torch.Tensor`, it should be of type ``torch.uint8``, and it is expected

torchvision/transforms/v2/_color.py

@@ -10,9 +10,7 @@
 
 
 class Grayscale(Transform):
- """[BETA] Convert images or videos to grayscale.
-
- .. v2betastatus:: Grayscale transform
+ """Convert images or videos to grayscale.
 
  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
@@ -32,9 +30,7 @@ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
 
 
 class RandomGrayscale(_RandomApplyTransform):
- """[BETA] Randomly convert image or videos to grayscale with a probability of p (default 0.1).
-
- .. v2betastatus:: RandomGrayscale transform
+ """Randomly convert image or videos to grayscale with a probability of p (default 0.1).
 
  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
@@ -59,9 +55,7 @@ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
 
 
 class ColorJitter(Transform):
- """[BETA] Randomly change the brightness, contrast, saturation and hue of an image or video.
-
- .. v2betastatus:: ColorJitter transform
+ """Randomly change the brightness, contrast, saturation and hue of an image or video.
 
  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.
@@ -163,10 +157,7 @@ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
 
 
 class RandomChannelPermutation(Transform):
- """[BETA] Randomly permute the channels of an image or video
-
- .. v2betastatus:: RandomChannelPermutation transform
- """
+ """Randomly permute the channels of an image or video"""
 
  def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
  num_channels, *_ = query_chw(flat_inputs)
@@ -177,11 +168,9 @@ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
 
 
 class RandomPhotometricDistort(Transform):
- """[BETA] Randomly distorts the image or video as used in `SSD: Single Shot
+ """Randomly distorts the image or video as used in `SSD: Single Shot
  MultiBox Detector <https://arxiv.org/abs/1512.02325>`_.
 
- .. v2betastatus:: RandomPhotometricDistort transform
-
  This transform relies on :class:`~torchvision.transforms.v2.ColorJitter`
  under the hood to adjust the contrast, saturation, hue, brightness, and also
  randomly permutes channels.
@@ -249,9 +238,7 @@ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
 
 
 class RandomEqualize(_RandomApplyTransform):
- """[BETA] Equalize the histogram of the given image or video with a given probability.
-
- .. v2betastatus:: RandomEqualize transform
+ """Equalize the histogram of the given image or video with a given probability.
 
  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.
@@ -268,9 +255,7 @@ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
 
 
 class RandomInvert(_RandomApplyTransform):
- """[BETA] Inverts the colors of the given image or video with a given probability.
-
- .. v2betastatus:: RandomInvert transform
+ """Inverts the colors of the given image or video with a given probability.
 
  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.
@@ -287,11 +272,9 @@ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
 
 
 class RandomPosterize(_RandomApplyTransform):
- """[BETA] Posterize the image or video with a given probability by reducing the
+ """Posterize the image or video with a given probability by reducing the
  number of bits for each color channel.
 
- .. 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.
  If img is PIL Image, it is expected to be in mode "L" or "RGB".
@@ -312,11 +295,9 @@ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
 
 
 class RandomSolarize(_RandomApplyTransform):
- """[BETA] Solarize the image or video with a given probability by inverting all pixel
+ """Solarize the image or video with a given probability by inverting all pixel
  values above a threshold.
 
- .. 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.
  If img is PIL Image, it is expected to be in mode "L" or "RGB".
@@ -342,9 +323,7 @@ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
 
 
 class RandomAutocontrast(_RandomApplyTransform):
- """[BETA] Autocontrast the pixels of the given image or video with a given probability.
-
- .. v2betastatus:: RandomAutocontrast transform
+ """Autocontrast the pixels of the given image or video with a given probability.
 
  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.
@@ -361,9 +340,7 @@ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
 
 
 class RandomAdjustSharpness(_RandomApplyTransform):
- """[BETA] Adjust the sharpness of the image or video with a given probability.
-
- .. v2betastatus:: RandomAdjustSharpness transform
+ """Adjust the sharpness of the image or video with a given probability.
 
  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

@@ -8,9 +8,7 @@
 
 
 class Compose(Transform):
- """[BETA] Composes several transforms together.
-
- .. v2betastatus:: Compose transform
+ """Composes several transforms together.
 
  This transform does not support torchscript.
  Please, see the note below.
@@ -62,9 +60,7 @@ def extra_repr(self) -> str:
 
 
 class RandomApply(Transform):
- """[BETA] Apply randomly a list of transformations with a given probability.
-
- .. v2betastatus:: RandomApply transform
+ """Apply randomly a list of transformations with a given probability.
 
  .. note::
  In order to script the transformation, please use ``torch.nn.ModuleList`` as input instead of list/tuple of
@@ -118,9 +114,7 @@ def extra_repr(self) -> str:
 
 
 class RandomChoice(Transform):
- """[BETA] Apply single transformation randomly picked from a list.
-
- .. v2betastatus:: RandomChoice transform
+ """Apply single transformation randomly picked from a list.
 
  This transform does not support torchscript.
 
@@ -157,9 +151,7 @@ def forward(self, *inputs: Any) -> Any:
 
 
 class RandomOrder(Transform):
- """[BETA] Apply a list of transformations in a random order.
-
- .. v2betastatus:: RandomOrder transform
+ """Apply a list of transformations in a random order.
 
  This transform does not support torchscript.
 

torchvision/transforms/v2/_deprecated.py

@@ -10,12 +10,10 @@
 
 
 class ToTensor(Transform):
- """[BETA] [DEPRECATED] Use ``v2.Compose([v2.ToImage(), v2.ToDtype(torch.float32, scale=True)])`` instead.
+ """[DEPRECATED] Use ``v2.Compose([v2.ToImage(), v2.ToDtype(torch.float32, scale=True)])`` instead.
 
  Convert a PIL Image or ndarray to tensor and scale the values accordingly.
 
- .. v2betastatus:: ToTensor transform
-
  .. warning::
  :class:`v2.ToTensor` is deprecated and will be removed in a future release.
  Please use instead ``v2.Compose([v2.ToImage(), v2.ToDtype(torch.float32, scale=True)])``.