remove most old v2 resize tests

Author: pmeier

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 @@ def fill_sequence_needs_broadcast(args_kwargs):
         },
         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

@@ -250,74 +250,11 @@ def _get_resize_sizes(spatial_size):
     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])
+            return
 
 
 def reference_resize_bounding_box(bounding_box, *, spatial_size, size, max_size=None):
@@ -352,36 +289,6 @@ def reference_inputs_resize_bounding_box():
 
 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,
@@ -394,19 +301,6 @@ def reference_inputs_resize_bounding_box():
                 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(),
-        ),
     ]
 )