[PoC] refactor transforms v2 tests (#7562)

Co-authored-by: Nicolas Hug <nh.nicolas.hug@gmail.com>
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

test/common_utils.py

@@ -7,9 +7,11 @@ import itertools
 import os
 import pathlib
 import random
+import re
 import shutil
 import sys
 import tempfile
+import warnings
 from collections import defaultdict
 from subprocess import CalledProcessError, check_output, STDOUT
 from typing import Callable, Sequence, Tuple, Union
@@ -880,3 +882,23 @@ def assert_run_python_script(source_code):
             raise RuntimeError(f"script errored with output:\n{e.output.decode()}")
         if out != b"":
             raise AssertionError(out.decode())
+
+
+@contextlib.contextmanager
+def assert_no_warnings():
+    # The name `catch_warnings` is a misnomer as the context manager does **not** catch any warnings, but rather scopes
+    # the warning filters. All changes that are made to the filters while in this context, will be reset upon exit.
+    with warnings.catch_warnings():
+        warnings.simplefilter("error")
+        yield
+
+
+@contextlib.contextmanager
+def ignore_jit_no_profile_information_warning():
+    # Calling a scripted object often triggers a warning like
+    # `UserWarning: operator() profile_node %$INT1 : int[] = prim::profile_ivalue($INT2) does not have profile information`
+    # with varying `INT1` and `INT2`. Since these are uninteresting for us and only clutter the test summary, we ignore
+    # them.
+    with warnings.catch_warnings():
+        warnings.filterwarnings("ignore", message=re.escape("operator() profile_node %"), category=UserWarning)
+        yield

test/test_transforms_v2.py

@@ -1711,8 +1711,6 @@ def test_antialias_warning():
     tensor_video = torch.randint(0, 256, size=(2, 3, 10, 10), dtype=torch.uint8)
 
     match = "The default value of the antialias parameter"
-    with pytest.warns(UserWarning, match=match):
-        transforms.Resize((20, 20))(tensor_img)
     with pytest.warns(UserWarning, match=match):
         transforms.RandomResizedCrop((20, 20))(tensor_img)
     with pytest.warns(UserWarning, match=match):
@@ -1722,18 +1720,6 @@ def test_antialias_warning():
     with pytest.warns(UserWarning, match=match):
         transforms.RandomResize(10, 20)(tensor_img)
 
-    with pytest.warns(UserWarning, match=match):
-        transforms.functional.resize(tensor_img, (20, 20))
-    with pytest.warns(UserWarning, match=match):
-        transforms.functional.resize_image_tensor(tensor_img, (20, 20))
-
-    with pytest.warns(UserWarning, match=match):
-        transforms.functional.resize(tensor_video, (20, 20))
-    with pytest.warns(UserWarning, match=match):
-        transforms.functional.resize_video(tensor_video, (20, 20))
-
-    with pytest.warns(UserWarning, match=match):
-        datapoints.Image(tensor_img).resize((20, 20))
     with pytest.warns(UserWarning, match=match):
         datapoints.Image(tensor_img).resized_crop(0, 0, 10, 10, (20, 20))
 
@@ -1744,27 +1730,17 @@ def test_antialias_warning():
 
     with warnings.catch_warnings():
         warnings.simplefilter("error")
-        transforms.Resize((20, 20))(pil_img)
         transforms.RandomResizedCrop((20, 20))(pil_img)
         transforms.ScaleJitter((20, 20))(pil_img)
         transforms.RandomShortestSize((20, 20))(pil_img)
         transforms.RandomResize(10, 20)(pil_img)
-        transforms.functional.resize(pil_img, (20, 20))
 
-        transforms.Resize((20, 20), antialias=True)(tensor_img)
         transforms.RandomResizedCrop((20, 20), antialias=True)(tensor_img)
         transforms.ScaleJitter((20, 20), antialias=True)(tensor_img)
         transforms.RandomShortestSize((20, 20), antialias=True)(tensor_img)
         transforms.RandomResize(10, 20, antialias=True)(tensor_img)
 
-        transforms.functional.resize(tensor_img, (20, 20), antialias=True)
-        transforms.functional.resize_image_tensor(tensor_img, (20, 20), antialias=True)
-        transforms.functional.resize(tensor_video, (20, 20), antialias=True)
-        transforms.functional.resize_video(tensor_video, (20, 20), antialias=True)
-
-        datapoints.Image(tensor_img).resize((20, 20), antialias=True)
         datapoints.Image(tensor_img).resized_crop(0, 0, 10, 10, (20, 20), antialias=True)
-        datapoints.Video(tensor_video).resize((20, 20), antialias=True)
         datapoints.Video(tensor_video).resized_crop(0, 0, 10, 10, (20, 20), antialias=True)
 
 

test/transforms_v2_dispatcher_infos.py

@@ -148,19 +148,6 @@ DISPATCHER_INFOS = [
         },
         pil_kernel_info=PILKernelInfo(F.horizontal_flip_image_pil, kernel_name="horizontal_flip_image_pil"),
     ),
-    DispatcherInfo(
-        F.resize,
-        kernels={
-            datapoints.Image: F.resize_image_tensor,
-            datapoints.Video: F.resize_video,
-            datapoints.BoundingBox: F.resize_bounding_box,
-            datapoints.Mask: F.resize_mask,
-        },
-        pil_kernel_info=PILKernelInfo(F.resize_image_pil),
-        test_marks=[
-            xfail_jit_python_scalar_arg("size"),
-        ],
-    ),
     DispatcherInfo(
         F.affine,
         kernels={

test/transforms_v2_kernel_infos.py

@@ -238,179 +238,6 @@ KERNEL_INFOS.extend(
 )
 
 
-def _get_resize_sizes(spatial_size):
-    height, width = spatial_size
-    length = max(spatial_size)
-    yield length
-    yield [length]
-    yield (length,)
-    new_height = int(height * 0.75)
-    new_width = int(width * 1.25)
-    yield [new_height, new_width]
-    yield height, width
-
-
-def sample_inputs_resize_image_tensor():
-    for image_loader in make_image_loaders(sizes=["random"], color_spaces=["RGB"], dtypes=[torch.float32]):
-        for size in _get_resize_sizes(image_loader.spatial_size):
-            yield ArgsKwargs(image_loader, size=size)
-
-    for image_loader, interpolation in itertools.product(
-        make_image_loaders(sizes=["random"], color_spaces=["RGB"]),
-        [F.InterpolationMode.NEAREST, F.InterpolationMode.BILINEAR],
-    ):
-        yield ArgsKwargs(image_loader, size=[min(image_loader.spatial_size) + 1], interpolation=interpolation)
-
-    yield ArgsKwargs(make_image_loader(size=(11, 17)), size=20, max_size=25)
-
-
-def sample_inputs_resize_image_tensor_bicubic():
-    for image_loader, interpolation in itertools.product(
-        make_image_loaders(sizes=["random"], color_spaces=["RGB"]), [F.InterpolationMode.BICUBIC]
-    ):
-        yield ArgsKwargs(image_loader, size=[min(image_loader.spatial_size) + 1], interpolation=interpolation)
-
-
-@pil_reference_wrapper
-def reference_resize_image_tensor(*args, **kwargs):
-    if not kwargs.pop("antialias", False) and kwargs.get("interpolation", F.InterpolationMode.BILINEAR) in {
-        F.InterpolationMode.BILINEAR,
-        F.InterpolationMode.BICUBIC,
-    }:
-        raise pytest.UsageError("Anti-aliasing is always active in PIL")
-    return F.resize_image_pil(*args, **kwargs)
-
-
-def reference_inputs_resize_image_tensor():
-    for image_loader, interpolation in itertools.product(
-        make_image_loaders_for_interpolation(),
-        [
-            F.InterpolationMode.NEAREST,
-            F.InterpolationMode.NEAREST_EXACT,
-            F.InterpolationMode.BILINEAR,
-            F.InterpolationMode.BICUBIC,
-        ],
-    ):
-        for size in _get_resize_sizes(image_loader.spatial_size):
-            yield ArgsKwargs(
-                image_loader,
-                size=size,
-                interpolation=interpolation,
-                antialias=interpolation
-                in {
-                    F.InterpolationMode.BILINEAR,
-                    F.InterpolationMode.BICUBIC,
-                },
-            )
-
-
-def sample_inputs_resize_bounding_box():
-    for bounding_box_loader in make_bounding_box_loaders():
-        for size in _get_resize_sizes(bounding_box_loader.spatial_size):
-            yield ArgsKwargs(bounding_box_loader, spatial_size=bounding_box_loader.spatial_size, size=size)
-
-
-def sample_inputs_resize_mask():
-    for mask_loader in make_mask_loaders(sizes=["random"], num_categories=["random"], num_objects=["random"]):
-        yield ArgsKwargs(mask_loader, size=[min(mask_loader.shape[-2:]) + 1])
-
-
-def sample_inputs_resize_video():
-    for video_loader in make_video_loaders(sizes=["random"], num_frames=["random"]):
-        yield ArgsKwargs(video_loader, size=[min(video_loader.shape[-2:]) + 1])
-
-
-def reference_resize_bounding_box(bounding_box, *, spatial_size, size, max_size=None):
-    old_height, old_width = spatial_size
-    new_height, new_width = F._geometry._compute_resized_output_size(spatial_size, size=size, max_size=max_size)
-
-    if (old_height, old_width) == (new_height, new_width):
-        return bounding_box, (old_height, old_width)
-
-    affine_matrix = np.array(
-        [
-            [new_width / old_width, 0, 0],
-            [0, new_height / old_height, 0],
-        ],
-        dtype="float64" if bounding_box.dtype == torch.float64 else "float32",
-    )
-
-    expected_bboxes = reference_affine_bounding_box_helper(
-        bounding_box,
-        format=bounding_box.format,
-        spatial_size=(new_height, new_width),
-        affine_matrix=affine_matrix,
-    )
-    return expected_bboxes, (new_height, new_width)
-
-
-def reference_inputs_resize_bounding_box():
-    for bounding_box_loader in make_bounding_box_loaders(extra_dims=((), (4,))):
-        for size in _get_resize_sizes(bounding_box_loader.spatial_size):
-            yield ArgsKwargs(bounding_box_loader, size=size, spatial_size=bounding_box_loader.spatial_size)
-
-
-KERNEL_INFOS.extend(
-    [
-        KernelInfo(
-            F.resize_image_tensor,
-            sample_inputs_fn=sample_inputs_resize_image_tensor,
-            reference_fn=reference_resize_image_tensor,
-            reference_inputs_fn=reference_inputs_resize_image_tensor,
-            float32_vs_uint8=True,
-            closeness_kwargs={
-                **pil_reference_pixel_difference(10, mae=True),
-                **cuda_vs_cpu_pixel_difference(),
-                **float32_vs_uint8_pixel_difference(1, mae=True),
-            },
-            test_marks=[
-                xfail_jit_python_scalar_arg("size"),
-            ],
-        ),
-        KernelInfo(
-            F.resize_image_tensor,
-            sample_inputs_fn=sample_inputs_resize_image_tensor_bicubic,
-            reference_fn=reference_resize_image_tensor,
-            reference_inputs_fn=reference_inputs_resize_image_tensor,
-            float32_vs_uint8=True,
-            closeness_kwargs={
-                **pil_reference_pixel_difference(10, mae=True),
-                **cuda_vs_cpu_pixel_difference(atol=30),
-                **float32_vs_uint8_pixel_difference(1, mae=True),
-            },
-            test_marks=[
-                xfail_jit_python_scalar_arg("size"),
-            ],
-        ),
-        KernelInfo(
-            F.resize_bounding_box,
-            sample_inputs_fn=sample_inputs_resize_bounding_box,
-            reference_fn=reference_resize_bounding_box,
-            reference_inputs_fn=reference_inputs_resize_bounding_box,
-            closeness_kwargs={
-                (("TestKernels", "test_against_reference"), torch.int64, "cpu"): dict(atol=1, rtol=0),
-            },
-            test_marks=[
-                xfail_jit_python_scalar_arg("size"),
-            ],
-        ),
-        KernelInfo(
-            F.resize_mask,
-            sample_inputs_fn=sample_inputs_resize_mask,
-            closeness_kwargs=pil_reference_pixel_difference(10),
-            test_marks=[
-                xfail_jit_python_scalar_arg("size"),
-            ],
-        ),
-        KernelInfo(
-            F.resize_video,
-            sample_inputs_fn=sample_inputs_resize_video,
-            closeness_kwargs=cuda_vs_cpu_pixel_difference(),
-        ),
-    ]
-)
-
-
 _AFFINE_KWARGS = combinations_grid(
     angle=[-87, 15, 90],
     translate=[(5, 5), (-5, -5)],