Remove import-time warning for v2 namespaces (#7853)

docs/source/conf.py

@@ -34,9 +34,6 @@ from tabulate import tabulate
 
 sys.path.append(os.path.abspath("."))
 
-torchvision.disable_beta_transforms_warning()
-import torchvision.datapoints  # Don't remove, otherwise the docs for datapoints aren't linked properly
-
 # -- General configuration ------------------------------------------------
 
 # Required version of sphinx is set from docs/requirements.txt

gallery/v2_transforms/plot_custom_datapoints.py

@@ -11,12 +11,6 @@ Torchvision v2 transforms. Before continuing, make sure you have read
 
 # %%
 import torch
-import torchvision
-
-# We are using BETA APIs, so we deactivate the associated warning, thereby acknowledging that
-# some APIs may slightly change in the future
-torchvision.disable_beta_transforms_warning()
-
 from torchvision import datapoints
 from torchvision.transforms import v2
 

gallery/v2_transforms/plot_custom_transforms.py

@@ -9,12 +9,6 @@ torchvision transforms V2 API.
 
 # %%
 import torch
-import torchvision
-
-# We are using BETA APIs, so we deactivate the associated warning, thereby acknowledging that
-# some APIs may slightly change in the future
-torchvision.disable_beta_transforms_warning()
-
 from torchvision import datapoints
 from torchvision.transforms import v2
 

gallery/v2_transforms/plot_cutmix_mixup.py

@@ -17,13 +17,7 @@ function.
 
 # %%
 import torch
-import torchvision
 from torchvision.datasets import FakeData
-
-# We are using BETA APIs, so we deactivate the associated warning, thereby acknowledging that
-# some APIs may slightly change in the future
-torchvision.disable_beta_transforms_warning()
-
 from torchvision.transforms import v2
 
 

gallery/v2_transforms/plot_datapoints.py

@@ -24,12 +24,6 @@ and how they behave.
 import PIL.Image
 
 import torch
-import torchvision
-
-# We are using BETA APIs, so we deactivate the associated warning, thereby acknowledging that
-# some APIs may slightly change in the future
-torchvision.disable_beta_transforms_warning()
-
 from torchvision import datapoints
 from torchvision.transforms.v2 import functional as F
 

gallery/v2_transforms/plot_transforms_v2.py

@@ -11,7 +11,6 @@ example showcases the core functionality of the new ``torchvision.transforms.v2`
 import pathlib
 
 import torch
-import torchvision
 
 
 def load_data():
@@ -42,9 +41,6 @@ def load_data():
 # detection or instance and semantic segmentation. Still, the interface is the same, making
 # :mod:`torchvision.transforms.v2` a drop-in replacement for the existing :mod:`torchvision.transforms` API, aka v1.
 
-# We are using BETA APIs, so we deactivate the associated warning, thereby acknowledging that
-# some APIs may slightly change in the future
-torchvision.disable_beta_transforms_warning()
 import torchvision.transforms.v2 as transforms
 
 transform = transforms.Compose(

gallery/v2_transforms/plot_transforms_v2_e2e.py

@@ -16,7 +16,8 @@ import PIL.Image
 import torch
 import torch.utils.data
 
-import torchvision
+from torchvision import models, datasets
+import torchvision.transforms.v2 as transforms
 
 
 def show(sample):
@@ -39,19 +40,10 @@ def show(sample):
     fig.show()
 
 
-# We are using BETA APIs, so we deactivate the associated warning, thereby acknowledging that
-# some APIs may slightly change in the future
-torchvision.disable_beta_transforms_warning()
-
-from torchvision import models, datasets
-import torchvision.transforms.v2 as transforms
-
-
 # %%
 # We start off by loading the :class:`~torchvision.datasets.CocoDetection` dataset to have a look at what it currently
 # returns, and we'll see how to convert it to a format that is compatible with our new transforms.
 
-
 def load_example_coco_detection_dataset(**kwargs):
     # This loads fake data for illustration purposes of this example. In practice, you'll have
     # to replace this with the proper data

test/conftest.py

@@ -3,10 +3,6 @@ import random
 import numpy as np
 import pytest
 import torch
-import torchvision
-
-
-torchvision.disable_beta_transforms_warning()
 
 from common_utils import (
     CUDA_NOT_AVAILABLE_MSG,

test/test_transforms_v2.py

 import itertools
 import pathlib
 import random
-import textwrap
 import warnings
 
 import numpy as np
@@ -11,7 +10,7 @@ import pytest
 import torch
 import torchvision.transforms.v2 as transforms
 
-from common_utils import assert_equal, assert_run_python_script, cpu_and_cuda
+from common_utils import assert_equal, cpu_and_cuda
 from torch.utils._pytree import tree_flatten, tree_unflatten
 from torchvision import datapoints
 from torchvision.ops.boxes import box_iou
@@ -1279,55 +1278,6 @@ def test_sanitize_bounding_boxes_errors():
         transforms.SanitizeBoundingBoxes()(different_sizes)
 
 
-@pytest.mark.parametrize(
-    "import_statement",
-    (
-        "from torchvision.transforms import v2",
-        "import torchvision.transforms.v2",
-        "from torchvision.transforms.v2 import Resize",
-        "import torchvision.transforms.v2.functional",
-        "from torchvision.transforms.v2.functional import resize",
-        "from torchvision import datapoints",
-        "from torchvision.datapoints import Image",
-        "from torchvision.datasets import wrap_dataset_for_transforms_v2",
-    ),
-)
-@pytest.mark.parametrize("call_disable_warning", (True, False))
-def test_warnings_v2_namespaces(import_statement, call_disable_warning):
-    if call_disable_warning:
-        source = f"""
-        import warnings
-        import torchvision
-        torchvision.disable_beta_transforms_warning()
-        with warnings.catch_warnings():
-            warnings.simplefilter("error")
-            {import_statement}
-        """
-    else:
-        source = f"""
-        import pytest
-        with pytest.warns(UserWarning, match="v2 namespaces are still Beta"):
-            {import_statement}
-        """
-    assert_run_python_script(textwrap.dedent(source))
-
-
-def test_no_warnings_v1_namespace():
-    source = """
-    import warnings
-    with warnings.catch_warnings():
-        warnings.simplefilter("error")
-        import torchvision.transforms
-        from torchvision import transforms
-        import torchvision.transforms.functional
-        from torchvision.transforms import Resize
-        from torchvision.transforms.functional import resize
-        from torchvision import datasets
-        from torchvision.datasets import ImageNet
-    """
-    assert_run_python_script(textwrap.dedent(source))
-
-
 class TestLambda:
     inputs = pytest.mark.parametrize("input", [object(), torch.empty(()), np.empty(()), "string", 1, 0.0])
 

torchvision/__init__.py

@@ -95,20 +95,3 @@ def get_video_backend():
 
 def _is_tracing():
     return torch._C._get_tracing_state()
-
-
-_WARN_ABOUT_BETA_TRANSFORMS = True
-_BETA_TRANSFORMS_WARNING = (
-    "The torchvision.datapoints and torchvision.transforms.v2 namespaces are still Beta. "
-    "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_transforms_warning()."
-)
-
-
-def disable_beta_transforms_warning():
-    global _WARN_ABOUT_BETA_TRANSFORMS
-    _WARN_ABOUT_BETA_TRANSFORMS = False

torchvision/datapoints/__init__.py

 import torch
-from torchvision import _BETA_TRANSFORMS_WARNING, _WARN_ABOUT_BETA_TRANSFORMS
 
 from ._bounding_box import BoundingBoxes, BoundingBoxFormat
 from ._datapoint import Datapoint
@@ -8,11 +7,6 @@ from ._mask import Mask
 from ._torch_function_helpers import set_return_type
 from ._video import Video
 
-if _WARN_ABOUT_BETA_TRANSFORMS:
-    import warnings
-
-    warnings.warn(_BETA_TRANSFORMS_WARNING)
-
 
 def wrap(wrappee, *, like, **kwargs):
     """[BETA] Convert a :class:`torch.Tensor` (``wrappee``) into the same :class:`~torchvision.datapoints.Datapoint` subclass as ``like``.

torchvision/datasets/__init__.py

@@ -127,14 +127,13 @@ __all__ = (
     "SintelStereo",
     "InStereo2k",
     "ETH3DStereo",
+    "wrap_dataset_for_transforms_v2",
 )
 
 
 # We override current module's attributes to handle the import:
 # from torchvision.datasets import wrap_dataset_for_transforms_v2
-# with beta state v2 warning from torchvision.datapoints
-# We also want to avoid raising the warning when importing other attributes
-# from torchvision.datasets
+# without a cyclic error.
 # Ref: https://peps.python.org/pep-0562/
 def __getattr__(name):
     if name in ("wrap_dataset_for_transforms_v2",):

torchvision/transforms/v2/__init__.py

@@ -55,10 +55,3 @@ from ._temporal import UniformTemporalSubsample
 from ._type_conversion import PILToTensor, ToImage, ToPILImage, ToPureTensor
 
 from ._deprecated import ToTensor  # usort: skip
-
-from torchvision import _BETA_TRANSFORMS_WARNING, _WARN_ABOUT_BETA_TRANSFORMS
-
-if _WARN_ABOUT_BETA_TRANSFORMS:
-    import warnings
-
-    warnings.warn(_BETA_TRANSFORMS_WARNING)