[proto] Added consistency tests for detection transforms (#6566)

* [proto] Added consistency tests for detection transforms

* Updated tests according to the review

* More updates
Co-authored-by: Philip Meier <github.pmeier@posteo.de>

test/test_prototype_transforms_consistency.py

 import enum
 import inspect
+from importlib.machinery import SourceFileLoader
+from pathlib import Path
 
 import numpy as np
 import PIL.Image
 import pytest
 
 import torch
-from prototype_common_utils import ArgsKwargs, assert_equal, make_images
+from prototype_common_utils import (
+    ArgsKwargs,
+    assert_equal,
+    make_bounding_box,
+    make_detection_mask,
+    make_image,
+    make_images,
+    make_label,
+)
 from torchvision import transforms as legacy_transforms
 from torchvision._utils import sequence_to_str
 from torchvision.prototype import features, transforms as prototype_transforms
@@ -840,3 +850,80 @@ class TestAATransforms:
         output = t(inpt)
 
         assert_equal(expected_output, output)
+
+
+# Import reference detection transforms here for consistency checks
+# torchvision/references/detection/transforms.py
+ref_det_filepath = Path(__file__).parent.parent / "references" / "detection" / "transforms.py"
+det_transforms = SourceFileLoader(ref_det_filepath.stem, ref_det_filepath.as_posix()).load_module()
+
+
+class TestRefDetTransforms:
+    def make_datapoints(self, with_mask=True):
+        size = (600, 800)
+        num_objects = 22
+
+        pil_image = to_image_pil(make_image(size=size, color_space=features.ColorSpace.RGB))
+        target = {
+            "boxes": make_bounding_box(image_size=size, format="XYXY", extra_dims=(num_objects,), dtype=torch.float),
+            "labels": make_label(extra_dims=(num_objects,), categories=80),
+        }
+        if with_mask:
+            target["masks"] = make_detection_mask(size=size, num_objects=num_objects, dtype=torch.long)
+
+        yield (pil_image, target)
+
+        tensor_image = torch.randint(0, 256, size=(3, *size), dtype=torch.uint8)
+        target = {
+            "boxes": make_bounding_box(image_size=size, format="XYXY", extra_dims=(num_objects,), dtype=torch.float),
+            "labels": make_label(extra_dims=(num_objects,), categories=80),
+        }
+        if with_mask:
+            target["masks"] = make_detection_mask(size=size, num_objects=num_objects, dtype=torch.long)
+
+        yield (tensor_image, target)
+
+        feature_image = features.Image(torch.randint(0, 256, size=(3, *size), dtype=torch.uint8))
+        target = {
+            "boxes": make_bounding_box(image_size=size, format="XYXY", extra_dims=(num_objects,), dtype=torch.float),
+            "labels": make_label(extra_dims=(num_objects,), categories=80),
+        }
+        if with_mask:
+            target["masks"] = make_detection_mask(size=size, num_objects=num_objects, dtype=torch.long)
+
+        yield (feature_image, target)
+
+    @pytest.mark.parametrize(
+        "t_ref, t, data_kwargs",
+        [
+            (det_transforms.RandomHorizontalFlip(p=1.0), prototype_transforms.RandomHorizontalFlip(p=1.0), {}),
+            (det_transforms.RandomIoUCrop(), prototype_transforms.RandomIoUCrop(), {"with_mask": False}),
+            (det_transforms.RandomZoomOut(), prototype_transforms.RandomZoomOut(), {"with_mask": False}),
+            (det_transforms.ScaleJitter((1024, 1024)), prototype_transforms.ScaleJitter((1024, 1024)), {}),
+            (
+                det_transforms.FixedSizeCrop((1024, 1024), fill=0),
+                prototype_transforms.FixedSizeCrop((1024, 1024), fill=0),
+                {},
+            ),
+            (
+                det_transforms.RandomShortestSize(
+                    min_size=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), max_size=1333
+                ),
+                prototype_transforms.RandomShortestSize(
+                    min_size=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), max_size=1333
+                ),
+                {},
+            ),
+        ],
+    )
+    def test_transform(self, t_ref, t, data_kwargs):
+        for dp in self.make_datapoints(**data_kwargs):
+
+            # We should use prototype transform first as reference transform performs inplace target update
+            torch.manual_seed(12)
+            output = t(dp)
+
+            torch.manual_seed(12)
+            expected_output = t_ref(*dp)
+
+            assert_equal(expected_output, output)