port convert_bounding_box_format tests (#7933)

test/test_transforms_v2_functional.py

@@ -522,33 +522,6 @@ class TestClampBoundingBoxes:
             F.clamp_bounding_boxes(tv_tensor, **metadata)
 
 
-class TestConvertFormatBoundingBoxes:
-    @pytest.mark.parametrize(
-        ("inpt", "old_format"),
-        [
-            (next(make_multiple_bounding_boxes()), None),
-            (next(make_multiple_bounding_boxes()).as_subclass(torch.Tensor), tv_tensors.BoundingBoxFormat.XYXY),
-        ],
-    )
-    def test_missing_new_format(self, inpt, old_format):
-        with pytest.raises(TypeError, match=re.escape("missing 1 required argument: 'new_format'")):
-            F.convert_bounding_box_format(inpt, old_format)
-
-    def test_pure_tensor_insufficient_metadata(self):
-        pure_tensor = next(make_multiple_bounding_boxes()).as_subclass(torch.Tensor)
-
-        with pytest.raises(ValueError, match=re.escape("`old_format` has to be passed")):
-            F.convert_bounding_box_format(pure_tensor, new_format=tv_tensors.BoundingBoxFormat.CXCYWH)
-
-    def test_tv_tensor_explicit_metadata(self):
-        tv_tensor = next(make_multiple_bounding_boxes())
-
-        with pytest.raises(ValueError, match=re.escape("`old_format` must not be passed")):
-            F.convert_bounding_box_format(
-                tv_tensor, old_format=tv_tensor.format, new_format=tv_tensors.BoundingBoxFormat.CXCYWH
-            )
-
-
 # TODO: All correctness checks below this line should be ported to be references on a `KernelInfo` in
 #  `transforms_v2_kernel_infos.py`
 

test/test_transforms_v2_refactored.py

 import contextlib
 import decimal
+import functools
 import inspect
+import itertools
 import math
 import pickle
 import re
@@ -12,6 +14,8 @@ import PIL.Image
 import pytest
 
 import torch
+
+import torchvision.ops
 import torchvision.transforms.v2 as transforms
 from common_utils import (
     assert_equal,
@@ -138,7 +142,6 @@ def check_kernel(
     check_cuda_vs_cpu=True,
     check_scripted_vs_eager=True,
     check_batched_vs_unbatched=True,
-    expect_same_dtype=True,
     **kwargs,
 ):
     initial_input_version = input._version
@@ -151,7 +154,7 @@ def check_kernel(
     # check that no inplace operation happened
     assert input._version == initial_input_version
 
-    if expect_same_dtype:
+    if kernel not in {F.to_dtype_image, F.to_dtype_video}:
         assert output.dtype == input.dtype
     assert output.device == input.device
 
@@ -187,7 +190,7 @@ def check_functional(functional, input, *args, check_scripted_smoke=True, **kwar
 
     assert isinstance(output, type(input))
 
-    if isinstance(input, tv_tensors.BoundingBoxes):
+    if isinstance(input, tv_tensors.BoundingBoxes) and functional is not F.convert_bounding_box_format:
         assert output.format == input.format
 
     if check_scripted_smoke:
@@ -264,7 +267,7 @@ def check_transform(transform, input, check_v1_compatibility=True):
     output = transform(input)
     assert isinstance(output, type(input))
 
-    if isinstance(input, tv_tensors.BoundingBoxes):
+    if isinstance(input, tv_tensors.BoundingBoxes) and not isinstance(transform, transforms.ConvertBoundingBoxFormat):
         assert output.format == input.format
 
     if check_v1_compatibility:
@@ -1743,7 +1746,6 @@ class TestToDtype:
         check_kernel(
             kernel,
             make_input(dtype=input_dtype, device=device),
-            expect_same_dtype=input_dtype is output_dtype,
             dtype=output_dtype,
             scale=scale,
         )
@@ -3009,3 +3011,102 @@ class TestAutoAugmentTransforms:
     def test_aug_mix_severity_error(self, severity):
         with pytest.raises(ValueError, match="severity must be between"):
             transforms.AugMix(severity=severity)
+
+
+class TestConvertBoundingBoxFormat:
+    old_new_formats = list(itertools.permutations(iter(tv_tensors.BoundingBoxFormat), 2))
+
+    @pytest.mark.parametrize(("old_format", "new_format"), old_new_formats)
+    def test_kernel(self, old_format, new_format):
+        check_kernel(
+            F.convert_bounding_box_format,
+            make_bounding_boxes(format=old_format),
+            new_format=new_format,
+            old_format=old_format,
+        )
+
+    @pytest.mark.parametrize("format", list(tv_tensors.BoundingBoxFormat))
+    @pytest.mark.parametrize("inplace", [False, True])
+    def test_kernel_noop(self, format, inplace):
+        input = make_bounding_boxes(format=format).as_subclass(torch.Tensor)
+        input_version = input._version
+
+        output = F.convert_bounding_box_format(input, old_format=format, new_format=format, inplace=inplace)
+
+        assert output is input
+        assert output.data_ptr() == input.data_ptr()
+        assert output._version == input_version
+
+    @pytest.mark.parametrize(("old_format", "new_format"), old_new_formats)
+    def test_kernel_inplace(self, old_format, new_format):
+        input = make_bounding_boxes(format=old_format).as_subclass(torch.Tensor)
+        input_version = input._version
+
+        output_out_of_place = F.convert_bounding_box_format(input, old_format=old_format, new_format=new_format)
+        assert output_out_of_place.data_ptr() != input.data_ptr()
+        assert output_out_of_place is not input
+
+        output_inplace = F.convert_bounding_box_format(
+            input, old_format=old_format, new_format=new_format, inplace=True
+        )
+        assert output_inplace.data_ptr() == input.data_ptr()
+        assert output_inplace._version > input_version
+        assert output_inplace is input
+
+        assert_equal(output_inplace, output_out_of_place)
+
+    @pytest.mark.parametrize(("old_format", "new_format"), old_new_formats)
+    def test_functional(self, old_format, new_format):
+        check_functional(F.convert_bounding_box_format, make_bounding_boxes(format=old_format), new_format=new_format)
+
+    @pytest.mark.parametrize(("old_format", "new_format"), old_new_formats)
+    @pytest.mark.parametrize("format_type", ["enum", "str"])
+    def test_transform(self, old_format, new_format, format_type):
+        check_transform(
+            transforms.ConvertBoundingBoxFormat(new_format.name if format_type == "str" else new_format),
+            make_bounding_boxes(format=old_format),
+        )
+
+    def _reference_convert_bounding_box_format(self, bounding_boxes, new_format):
+        return tv_tensors.wrap(
+            torchvision.ops.box_convert(
+                bounding_boxes.as_subclass(torch.Tensor),
+                in_fmt=bounding_boxes.format.name.lower(),
+                out_fmt=new_format.name.lower(),
+            ).to(bounding_boxes.dtype),
+            like=bounding_boxes,
+            format=new_format,
+        )
+
+    @pytest.mark.parametrize(("old_format", "new_format"), old_new_formats)
+    @pytest.mark.parametrize("dtype", [torch.int64, torch.float32])
+    @pytest.mark.parametrize("device", cpu_and_cuda())
+    @pytest.mark.parametrize("fn_type", ["functional", "transform"])
+    def test_correctness(self, old_format, new_format, dtype, device, fn_type):
+        bounding_boxes = make_bounding_boxes(format=old_format, dtype=dtype, device=device)
+
+        if fn_type == "functional":
+            fn = functools.partial(F.convert_bounding_box_format, new_format=new_format)
+        else:
+            fn = transforms.ConvertBoundingBoxFormat(format=new_format)
+
+        actual = fn(bounding_boxes)
+        expected = self._reference_convert_bounding_box_format(bounding_boxes, new_format)
+
+        assert_equal(actual, expected)
+
+    def test_errors(self):
+        input_tv_tensor = make_bounding_boxes()
+        input_pure_tensor = input_tv_tensor.as_subclass(torch.Tensor)
+
+        for input in [input_tv_tensor, input_pure_tensor]:
+            with pytest.raises(TypeError, match="missing 1 required argument: 'new_format'"):
+                F.convert_bounding_box_format(input)
+
+        with pytest.raises(ValueError, match="`old_format` has to be passed"):
+            F.convert_bounding_box_format(input_pure_tensor, new_format=input_tv_tensor.format)
+
+        with pytest.raises(ValueError, match="`old_format` must not be passed"):
+            F.convert_bounding_box_format(
+                input_tv_tensor, old_format=input_tv_tensor.format, new_format=input_tv_tensor.format
+            )

test/transforms_v2_dispatcher_infos.py

@@ -315,11 +315,4 @@ DISPATCHER_INFOS = [
             skip_dispatch_tv_tensor,
         ],
     ),
-    DispatcherInfo(
-        F.convert_bounding_box_format,
-        kernels={tv_tensors.BoundingBoxes: F.convert_bounding_box_format},
-        test_marks=[
-            skip_dispatch_tv_tensor,
-        ],
-    ),
 ]

test/transforms_v2_kernel_infos.py

@@ -5,7 +5,6 @@ import numpy as np
 import PIL.Image
 import pytest
 import torch.testing
-import torchvision.ops
 import torchvision.transforms.v2.functional as F
 from torchvision import tv_tensors
 from torchvision.transforms._functional_tensor import _max_value as get_max_value, _parse_pad_padding
@@ -227,38 +226,6 @@ def reference_affine_bounding_boxes_helper(bounding_boxes, *, format, canvas_siz
     ).reshape(bounding_boxes.shape)
 
 
-def sample_inputs_convert_bounding_box_format():
-    formats = list(tv_tensors.BoundingBoxFormat)
-    for bounding_boxes_loader, new_format in itertools.product(make_bounding_box_loaders(formats=formats), formats):
-        yield ArgsKwargs(bounding_boxes_loader, old_format=bounding_boxes_loader.format, new_format=new_format)
-
-
-def reference_convert_bounding_box_format(bounding_boxes, old_format, new_format):
-    return torchvision.ops.box_convert(
-        bounding_boxes, in_fmt=old_format.name.lower(), out_fmt=new_format.name.lower()
-    ).to(bounding_boxes.dtype)
-
-
-def reference_inputs_convert_bounding_box_format():
-    for args_kwargs in sample_inputs_convert_bounding_box_format():
-        if len(args_kwargs.args[0].shape) == 2:
-            yield args_kwargs
-
-
-KERNEL_INFOS.append(
-    KernelInfo(
-        F.convert_bounding_box_format,
-        sample_inputs_fn=sample_inputs_convert_bounding_box_format,
-        reference_fn=reference_convert_bounding_box_format,
-        reference_inputs_fn=reference_inputs_convert_bounding_box_format,
-        logs_usage=True,
-        closeness_kwargs={
-            (("TestKernels", "test_against_reference"), torch.int64, "cpu"): dict(atol=1, rtol=0),
-        },
-    ),
-)
-
-
 _RESIZED_CROP_PARAMS = combinations_grid(top=[-8, 9], left=[-8, 9], height=[12], width=[12], size=[(16, 18)])