cleanup legacy transforms tests (#8013)

test/common_utils.py

@@ -406,6 +406,7 @@ def make_bounding_boxes(
     canvas_size=DEFAULT_SIZE,
     *,
     format=tv_tensors.BoundingBoxFormat.XYXY,
+    num_boxes=1,
     dtype=None,
     device="cpu",
 ):
@@ -419,8 +420,7 @@ def make_bounding_boxes(
 
     dtype = dtype or torch.float32
 
-    num_objects = 1
-    h, w = [torch.randint(1, s, (num_objects,)) for s in canvas_size]
+    h, w = [torch.randint(1, s, (num_boxes,)) for s in canvas_size]
     y = sample_position(h, canvas_size[0])
     x = sample_position(w, canvas_size[1])
 
@@ -443,12 +443,11 @@ def make_bounding_boxes(
     )
 
 
-def make_detection_mask(size=DEFAULT_SIZE, *, dtype=None, device="cpu"):
+def make_detection_masks(size=DEFAULT_SIZE, *, num_masks=1, dtype=None, device="cpu"):
     """Make a "detection" mask, i.e. (*, N, H, W), where each object is encoded as one of N boolean masks"""
-    num_objects = 1
     return tv_tensors.Mask(
         torch.testing.make_tensor(
-            (num_objects, *size),
+            (num_masks, *size),
             low=0,
             high=2,
             dtype=dtype or torch.bool,

test/test_transforms_v2_consistency.py

 import importlib.machinery
 import importlib.util
-import inspect
 import random
-import re
 from pathlib import Path
 
-import numpy as np
 import pytest
 
 import torch
 import torchvision.transforms.v2 as v2_transforms
-from common_utils import assert_close, assert_equal, set_rng_seed
-from torchvision import transforms as legacy_transforms, tv_tensors
+from common_utils import assert_equal
+from torchvision import tv_tensors
 
 from torchvision.transforms import functional as legacy_F
 from torchvision.transforms.v2 import functional as prototype_F
 from torchvision.transforms.v2._utils import _get_fill, query_size
 from torchvision.transforms.v2.functional import to_pil_image
-from transforms_v2_legacy_utils import (
-    ArgsKwargs,
-    make_bounding_boxes,
-    make_detection_mask,
-    make_image,
-    make_images,
-    make_segmentation_mask,
-)
-
-DEFAULT_MAKE_IMAGES_KWARGS = dict(color_spaces=["RGB"], extra_dims=[(4,)])
-
-
-@pytest.fixture(autouse=True)
-def fix_rng_seed():
-    set_rng_seed(0)
-    yield
-
-
-class NotScriptableArgsKwargs(ArgsKwargs):
-    """
-    This class is used to mark parameters that render the transform non-scriptable. They still work in eager mode and
-    thus will be tested there, but will be skipped by the JIT tests.
-    """
-
-    pass
-
-
-class ConsistencyConfig:
-    def __init__(
-        self,
-        prototype_cls,
-        legacy_cls,
-        # If no args_kwargs is passed, only the signature will be checked
-        args_kwargs=(),
-        make_images_kwargs=None,
-        supports_pil=True,
-        removed_params=(),
-        closeness_kwargs=None,
-    ):
-        self.prototype_cls = prototype_cls
-        self.legacy_cls = legacy_cls
-        self.args_kwargs = args_kwargs
-        self.make_images_kwargs = make_images_kwargs or DEFAULT_MAKE_IMAGES_KWARGS
-        self.supports_pil = supports_pil
-        self.removed_params = removed_params
-        self.closeness_kwargs = closeness_kwargs or dict(rtol=0, atol=0)
-
-
-# These are here since both the prototype and legacy transform need to be constructed with the same random parameters
-LINEAR_TRANSFORMATION_MEAN = torch.rand(36)
-LINEAR_TRANSFORMATION_MATRIX = torch.rand([LINEAR_TRANSFORMATION_MEAN.numel()] * 2)
-
-CONSISTENCY_CONFIGS = []
-
-
-def check_call_consistency(
-    prototype_transform, legacy_transform, images=None, supports_pil=True, closeness_kwargs=None
-):
-    if images is None:
-        images = make_images(**DEFAULT_MAKE_IMAGES_KWARGS)
-
-    closeness_kwargs = closeness_kwargs or dict()
-
-    for image in images:
-        image_repr = f"[{tuple(image.shape)}, {str(image.dtype).rsplit('.')[-1]}]"
-
-        image_tensor = torch.Tensor(image)
-        try:
-            torch.manual_seed(0)
-            output_legacy_tensor = legacy_transform(image_tensor)
-        except Exception as exc:
-            raise pytest.UsageError(
-                f"Transforming a tensor image {image_repr} failed in the legacy transform with the "
-                f"error above. This means that you need to specify the parameters passed to `make_images` through the "
-                "`make_images_kwargs` of the `ConsistencyConfig`."
-            ) from exc
-
-        try:
-            torch.manual_seed(0)
-            output_prototype_tensor = prototype_transform(image_tensor)
-        except Exception as exc:
-            raise AssertionError(
-                f"Transforming a tensor image with shape {image_repr} failed in the prototype transform with "
-                f"the error above. This means there is a consistency bug either in `_get_params` or in the "
-                f"`is_pure_tensor` path in `_transform`."
-            ) from exc
-
-        assert_close(
-            output_prototype_tensor,
-            output_legacy_tensor,
-            msg=lambda msg: f"Tensor image consistency check failed with: \n\n{msg}",
-            **closeness_kwargs,
-        )
-
-        try:
-            torch.manual_seed(0)
-            output_prototype_image = prototype_transform(image)
-        except Exception as exc:
-            raise AssertionError(
-                f"Transforming a image tv_tensor with shape {image_repr} failed in the prototype transform with "
-                f"the error above. This means there is a consistency bug either in `_get_params` or in the "
-                f"`tv_tensors.Image` path in `_transform`."
-            ) from exc
-
-        assert_close(
-            output_prototype_image,
-            output_prototype_tensor,
-            msg=lambda msg: f"Output for tv_tensor and tensor images is not equal: \n\n{msg}",
-            **closeness_kwargs,
-        )
-
-        if image.ndim == 3 and supports_pil:
-            image_pil = to_pil_image(image)
-
-            try:
-                torch.manual_seed(0)
-                output_legacy_pil = legacy_transform(image_pil)
-            except Exception as exc:
-                raise pytest.UsageError(
-                    f"Transforming a PIL image with shape {image_repr} failed in the legacy transform with the "
-                    f"error above. If this transform does not support PIL images, set `supports_pil=False` on the "
-                    "`ConsistencyConfig`. "
-                ) from exc
-
-            try:
-                torch.manual_seed(0)
-                output_prototype_pil = prototype_transform(image_pil)
-            except Exception as exc:
-                raise AssertionError(
-                    f"Transforming a PIL image with shape {image_repr} failed in the prototype transform with "
-                    f"the error above. This means there is a consistency bug either in `_get_params` or in the "
-                    f"`PIL.Image.Image` path in `_transform`."
-                ) from exc
-
-            assert_close(
-                output_prototype_pil,
-                output_legacy_pil,
-                msg=lambda msg: f"PIL image consistency check failed with: \n\n{msg}",
-                **closeness_kwargs,
-            )
-
-
-@pytest.mark.parametrize(
-    ("config", "args_kwargs"),
-    [
-        pytest.param(
-            config, args_kwargs, id=f"{config.legacy_cls.__name__}-{idx:0{len(str(len(config.args_kwargs)))}d}"
-        )
-        for config in CONSISTENCY_CONFIGS
-        for idx, args_kwargs in enumerate(config.args_kwargs)
-    ],
-)
-@pytest.mark.filterwarnings("ignore")
-def test_call_consistency(config, args_kwargs):
-    args, kwargs = args_kwargs
-
-    try:
-        legacy_transform = config.legacy_cls(*args, **kwargs)
-    except Exception as exc:
-        raise pytest.UsageError(
-            f"Initializing the legacy transform failed with the error above. "
-            f"Please correct the `ArgsKwargs({args_kwargs})` in the `ConsistencyConfig`."
-        ) from exc
-
-    try:
-        prototype_transform = config.prototype_cls(*args, **kwargs)
-    except Exception as exc:
-        raise AssertionError(
-            "Initializing the prototype transform failed with the error above. "
-            "This means there is a consistency bug in the constructor."
-        ) from exc
-
-    check_call_consistency(
-        prototype_transform,
-        legacy_transform,
-        images=make_images(**config.make_images_kwargs),
-        supports_pil=config.supports_pil,
-        closeness_kwargs=config.closeness_kwargs,
-    )
-
-
-@pytest.mark.parametrize(
-    ("config", "args_kwargs"),
-    [
-        pytest.param(
-            config, args_kwargs, id=f"{config.legacy_cls.__name__}-{idx:0{len(str(len(config.args_kwargs)))}d}"
-        )
-        for config in CONSISTENCY_CONFIGS
-        for idx, args_kwargs in enumerate(config.args_kwargs)
-        if not isinstance(args_kwargs, NotScriptableArgsKwargs)
-    ],
-)
-def test_jit_consistency(config, args_kwargs):
-    args, kwargs = args_kwargs
-
-    prototype_transform_eager = config.prototype_cls(*args, **kwargs)
-    legacy_transform_eager = config.legacy_cls(*args, **kwargs)
-
-    legacy_transform_scripted = torch.jit.script(legacy_transform_eager)
-    prototype_transform_scripted = torch.jit.script(prototype_transform_eager)
-
-    for image in make_images(**config.make_images_kwargs):
-        image = image.as_subclass(torch.Tensor)
-
-        torch.manual_seed(0)
-        output_legacy_scripted = legacy_transform_scripted(image)
-
-        torch.manual_seed(0)
-        output_prototype_scripted = prototype_transform_scripted(image)
 
-        assert_close(output_prototype_scripted, output_legacy_scripted, **config.closeness_kwargs)
-
-
-class TestToTensorTransforms:
-    def test_pil_to_tensor(self):
-        prototype_transform = v2_transforms.PILToTensor()
-        legacy_transform = legacy_transforms.PILToTensor()
-
-        for image in make_images(extra_dims=[()]):
-            image_pil = to_pil_image(image)
-
-            assert_equal(prototype_transform(image_pil), legacy_transform(image_pil))
-
-    def test_to_tensor(self):
-        with pytest.warns(UserWarning, match=re.escape("The transform `ToTensor()` is deprecated")):
-            prototype_transform = v2_transforms.ToTensor()
-        legacy_transform = legacy_transforms.ToTensor()
-
-        for image in make_images(extra_dims=[()]):
-            image_pil = to_pil_image(image)
-            image_numpy = np.array(image_pil)
-
-            assert_equal(prototype_transform(image_pil), legacy_transform(image_pil))
-            assert_equal(prototype_transform(image_numpy), legacy_transform(image_numpy))
+from transforms_v2_legacy_utils import make_bounding_boxes, make_detection_mask, make_image, make_segmentation_mask
 
 
 def import_transforms_from_references(reference):
@@ -447,54 +212,3 @@ class TestRefSegTransforms:
     )
     def test_common(self, t_ref, t, data_kwargs):
         self.check(t, t_ref, data_kwargs)
-
-
-@pytest.mark.parametrize(
-    ("legacy_dispatcher", "name_only_params"),
-    [
-        (legacy_F.get_dimensions, {}),
-        (legacy_F.get_image_size, {}),
-        (legacy_F.get_image_num_channels, {}),
-        (legacy_F.to_tensor, {}),
-        (legacy_F.pil_to_tensor, {}),
-        (legacy_F.convert_image_dtype, {}),
-        (legacy_F.to_pil_image, {}),
-        (legacy_F.to_grayscale, {}),
-        (legacy_F.rgb_to_grayscale, {}),
-        (legacy_F.to_tensor, {}),
-    ],
-)
-def test_dispatcher_signature_consistency(legacy_dispatcher, name_only_params):
-    legacy_signature = inspect.signature(legacy_dispatcher)
-    legacy_params = list(legacy_signature.parameters.values())[1:]
-
-    try:
-        prototype_dispatcher = getattr(prototype_F, legacy_dispatcher.__name__)
-    except AttributeError:
-        raise AssertionError(
-            f"Legacy dispatcher `F.{legacy_dispatcher.__name__}` has no prototype equivalent"
-        ) from None
-
-    prototype_signature = inspect.signature(prototype_dispatcher)
-    prototype_params = list(prototype_signature.parameters.values())[1:]
-
-    # Some dispatchers got extra parameters. This makes sure they have a default argument and thus are BC. We don't
-    # need to check if parameters were added in the middle rather than at the end, since that will be caught by the
-    # regular check below.
-    prototype_params, new_prototype_params = (
-        prototype_params[: len(legacy_params)],
-        prototype_params[len(legacy_params) :],
-    )
-    for param in new_prototype_params:
-        assert param.default is not param.empty
-
-    # Some annotations were changed mostly to supersets of what was there before. Plus, some legacy dispatchers had no
-    # annotations. In these cases we simply drop the annotation and default argument from the comparison
-    for prototype_param, legacy_param in zip(prototype_params, legacy_params):
-        if legacy_param.name in name_only_params:
-            prototype_param._annotation = prototype_param._default = inspect.Parameter.empty
-            legacy_param._annotation = legacy_param._default = inspect.Parameter.empty
-        elif legacy_param.annotation is inspect.Parameter.empty:
-            prototype_param._annotation = inspect.Parameter.empty
-
-    assert prototype_params == legacy_params

test/test_transforms_v2_functional.py

-import numpy as np
-import PIL.Image
-import pytest
-import torch
-
-from torchvision.transforms.v2 import functional as F
-
-
-@pytest.mark.parametrize(
-    ("alias", "target"),
-    [
-        pytest.param(alias, target, id=alias.__name__)
-        for alias, target in [
-            (F.hflip, F.horizontal_flip),
-            (F.vflip, F.vertical_flip),
-            (F.get_image_num_channels, F.get_num_channels),
-            (F.to_pil_image, F.to_pil_image),
-            (F.elastic_transform, F.elastic),
-            (F.to_grayscale, F.rgb_to_grayscale),
-        ]
-    ],
-)
-def test_alias(alias, target):
-    assert alias is target
-
-
-@pytest.mark.parametrize(
-    "inpt",
-    [
-        127 * np.ones((32, 32, 3), dtype="uint8"),
-        PIL.Image.new("RGB", (32, 32), 122),
-    ],
-)
-def test_to_image(inpt):
-    output = F.to_image(inpt)
-    assert isinstance(output, torch.Tensor)
-    assert output.shape == (3, 32, 32)
-
-    assert np.asarray(inpt).sum() == output.sum().item()
-
-
-@pytest.mark.parametrize(
-    "inpt",
-    [
-        torch.randint(0, 256, size=(3, 32, 32), dtype=torch.uint8),
-        127 * np.ones((32, 32, 3), dtype="uint8"),
-    ],
-)
-@pytest.mark.parametrize("mode", [None, "RGB"])
-def test_to_pil_image(inpt, mode):
-    output = F.to_pil_image(inpt, mode=mode)
-    assert isinstance(output, PIL.Image.Image)
-
-    assert np.asarray(inpt).sum() == np.asarray(output).sum()

test/test_transforms_v2_utils.py

@@ -4,7 +4,7 @@ import pytest
 import torch
 
 import torchvision.transforms.v2._utils
-from common_utils import DEFAULT_SIZE, make_bounding_boxes, make_detection_mask, make_image
+from common_utils import DEFAULT_SIZE, make_bounding_boxes, make_detection_masks, make_image
 
 from torchvision import tv_tensors
 from torchvision.transforms.v2._utils import has_all, has_any
@@ -13,7 +13,7 @@ from torchvision.transforms.v2.functional import to_pil_image
 
 IMAGE = make_image(DEFAULT_SIZE, color_space="RGB")
 BOUNDING_BOX = make_bounding_boxes(DEFAULT_SIZE, format=tv_tensors.BoundingBoxFormat.XYXY)
-MASK = make_detection_mask(DEFAULT_SIZE)
+MASK = make_detection_masks(DEFAULT_SIZE)
 
 
 @pytest.mark.parametrize(

test/transforms_v2_legacy_utils.py

@@ -5,12 +5,8 @@ implemented there and must not use any of the utilities here.
 
 The following legacy modules depend on this module
 
-- test_transforms_v2_functional.py
 - test_transforms_v2_consistency.py
-- test_transforms_v2.py
-
-When all the logic is ported from the files above to test_transforms_v2_refactored.py, delete
-all the legacy modules including this one and drop the _refactored prefix from the name.
+- test_prototype_transforms.py
 """
 
 import collections.abc