Add warnings checks for v2 namespaces

test/common_utils.py

@@ -8,8 +8,10 @@
 import pathlib
 import random
 import shutil
+import sys
 import tempfile
 from collections import defaultdict
+from subprocess import CalledProcessError, check_output, STDOUT
 from typing import Callable, Sequence, Tuple, Union
 
 import numpy as np
@@ -838,3 +840,22 @@ def get_closeness_kwargs(self, test_id, *, dtype, device):
         if isinstance(device, torch.device):
             device = device.type
         return self.closeness_kwargs.get((test_id, dtype, device), dict())
+
+
+def assert_run_python_script(source_code):
+    """Utility to check assertions in an independent Python subprocess.
+    The script provided in the source code should return 0 and not print
+    anything on stderr or stdout. Taken from scikit-learn test utils.
+    source_code (str): The Python source code to execute.
+    """
+    with tempfile.NamedTemporaryFile(mode="wb") as f:
+        f.write(source_code.encode())
+        f.flush()
+
+        cmd = [sys.executable, f.name]
+        try:
+            out = check_output(cmd, stderr=STDOUT)
+        except CalledProcessError as e:
+            raise RuntimeError("script errored with output:\n%s" % e.output.decode())
+        if out != b"":
+            raise AssertionError(out.decode())

test/conftest.py

@@ -4,11 +4,12 @@
 import pytest
 import torch
 import torchvision
-from common_utils import CUDA_NOT_AVAILABLE_MSG, IN_FBCODE, IN_OSS_CI, IN_RE_WORKER, OSS_CI_GPU_NO_CUDA_MSG
 
 
 torchvision.disable_beta_transforms_warning()
 
+from common_utils import CUDA_NOT_AVAILABLE_MSG, IN_FBCODE, IN_OSS_CI, IN_RE_WORKER, OSS_CI_GPU_NO_CUDA_MSG
+
 
 def pytest_configure(config):
     # register an additional marker (see pytest_collection_modifyitems)

test/test_transforms.py

@@ -2,6 +2,7 @@
 import os
 import random
 import re
+import textwrap
 import warnings
 from functools import partial
 
@@ -24,7 +25,7 @@
 except ImportError:
     stats = None
 
-from common_utils import assert_equal, cycle_over, float_dtypes, int_dtypes
+from common_utils import assert_equal, assert_run_python_script, cycle_over, float_dtypes, int_dtypes
 
 
 GRACE_HOPPER = get_file_path_2(
@@ -2266,5 +2267,49 @@ def test_random_grayscale_with_grayscale_input():
     torch.testing.assert_close(F.pil_to_tensor(output_pil), image_tensor)
 
 
+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
+    """
+    assert_run_python_script(textwrap.dedent(source))
+
+
+# TODO: remove in 0.17 when we can delete functional_pil.py and functional_tensor.py
+@pytest.mark.parametrize(
+    "import_statement",
+    (
+        "from torchvision.transforms import functional_pil",
+        "from torchvision.transforms import functional_tensor",
+        "from torchvision.transforms.functional_tensor import resize",
+        "from torchvision.transforms.functional_pil import resize",
+    ),
+)
+@pytest.mark.parametrize("from_private", (True, False))
+def test_functional_deprecation_warning(import_statement, from_private):
+    if from_private:
+        import_statement = import_statement.replace("functional", "_functional")
+        prelude = """
+        import warnings
+
+        with warnings.catch_warnings():
+            warnings.simplefilter("error")
+        """
+    else:
+        prelude = """
+        import pytest
+        with pytest.warns(UserWarning, match="removed in 0.17"):
+        """
+
+    source = prelude + " " * 4 + import_statement
+    assert_run_python_script(textwrap.dedent(source))
+
+
 if __name__ == "__main__":
     pytest.main([__file__])

test/test_transforms_v2.py

@@ -2,6 +2,7 @@
 import pathlib
 import random
 import re
+import textwrap
 import warnings
 from collections import defaultdict
 
@@ -14,6 +15,7 @@
 
 from common_utils import (
     assert_equal,
+    assert_run_python_script,
     cpu_and_gpu,
     make_bounding_box,
     make_bounding_boxes,
@@ -2045,3 +2047,52 @@ def test_sanitize_bounding_boxes_errors():
         )
         different_sizes = {"bbox": bad_bbox, "labels": torch.arange(bad_bbox.shape[0])}
         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:
+        prelude = """
+        import warnings
+        import torchvision
+        torchvision.disable_beta_transforms_warning()
+        with warnings.catch_warnings():
+            warnings.simplefilter("error")
+
+        """
+    else:
+        prelude = """
+        import pytest
+        with pytest.warns(UserWarning, match="v2 namespaces are still Beta"):
+        """
+    source = prelude + " " * 4 + 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))