Support encoded RLE format in for COCO segmentations

test/test_datasets.py

@@ -782,32 +782,46 @@ def inject_fake_data(self, tmpdir, config):
 
         annotation_folder = tmpdir / self._ANNOTATIONS_FOLDER
         os.makedirs(annotation_folder)
+
+        segmentation_kind = config.pop("segmentation_kind", "list")
         info = self._create_annotation_file(
-            annotation_folder, self._ANNOTATIONS_FILE, file_names, num_annotations_per_image
+            annotation_folder,
+            self._ANNOTATIONS_FILE,
+            file_names,
+            num_annotations_per_image,
+            segmentation_kind=segmentation_kind,
         )
 
         info["num_examples"] = num_images
         return info
 
-    def _create_annotation_file(self, root, name, file_names, num_annotations_per_image):
+    def _create_annotation_file(self, root, name, file_names, num_annotations_per_image, segmentation_kind="list"):
         image_ids = [int(file_name.stem) for file_name in file_names]
         images = [dict(file_name=str(file_name), id=id) for file_name, id in zip(file_names, image_ids)]
 
-        annotations, info = self._create_annotations(image_ids, num_annotations_per_image)
+        annotations, info = self._create_annotations(image_ids, num_annotations_per_image, segmentation_kind)
         self._create_json(root, name, dict(images=images, annotations=annotations))
 
         return info
 
-    def _create_annotations(self, image_ids, num_annotations_per_image):
+    def _create_annotations(self, image_ids, num_annotations_per_image, segmentation_kind="list"):
         annotations = []
         annotion_id = 0
+
         for image_id in itertools.islice(itertools.cycle(image_ids), len(image_ids) * num_annotations_per_image):
+            segmentation = {
+                "list": [torch.rand(8).tolist()],
+                "rle": {"size": [10, 10], "counts": [1]},
+                "rle_encoded": {"size": [2400, 2400], "counts": "PQRQ2[1\\Y2f0gNVNRhMg2"},
+                "bad": 123,
+            }[segmentation_kind]
+
             annotations.append(
                 dict(
                     image_id=image_id,
                     id=annotion_id,
                     bbox=torch.rand(4).tolist(),
-                    segmentation=[torch.rand(8).tolist()],
+                    segmentation=segmentation,
                     category_id=int(torch.randint(91, ())),
                     area=float(torch.rand(1)),
                     iscrowd=int(torch.randint(2, size=(1,))),
@@ -832,11 +846,27 @@ def test_slice_error(self):
             with pytest.raises(ValueError, match="Index must be of type integer"):
                 dataset[:2]
 
+    def test_segmentation_kind(self):
+        if isinstance(self, CocoCaptionsTestCase):
+            return
+
+        for segmentation_kind in ("list", "rle", "rle_encoded"):
+            config = {"segmentation_kind": segmentation_kind}
+            with self.create_dataset(config) as (dataset, _):
+                dataset = datasets.wrap_dataset_for_transforms_v2(dataset, target_keys="all")
+                list(dataset)
+
+        config = {"segmentation_kind": "bad"}
+        with self.create_dataset(config) as (dataset, _):
+            dataset = datasets.wrap_dataset_for_transforms_v2(dataset, target_keys="all")
+            with pytest.raises(ValueError, match="COCO segmentation expected to be a dict or a list"):
+                list(dataset)
+
 
 class CocoCaptionsTestCase(CocoDetectionTestCase):
     DATASET_CLASS = datasets.CocoCaptions
 
-    def _create_annotations(self, image_ids, num_annotations_per_image):
+    def _create_annotations(self, image_ids, num_annotations_per_image, segmentation_kind="list"):
         captions = [str(idx) for idx in range(num_annotations_per_image)]
         annotations = combinations_grid(image_id=image_ids, caption=captions)
         for id, annotation in enumerate(annotations):

torchvision/tv_tensors/_dataset_wrapper.py

@@ -359,11 +359,14 @@ def coco_dectection_wrapper_factory(dataset, target_keys):
     def segmentation_to_mask(segmentation, *, canvas_size):
         from pycocotools import mask
 
-        segmentation = (
-            mask.frPyObjects(segmentation, *canvas_size)
-            if isinstance(segmentation, dict)
-            else mask.merge(mask.frPyObjects(segmentation, *canvas_size))
-        )
+        if isinstance(segmentation, dict):
+            # if counts is a string, it is already an encoded RLE mask
+            if not isinstance(segmentation["counts"], str):
+                segmentation = mask.frPyObjects(segmentation, *canvas_size)
+        elif isinstance(segmentation, list):
+            segmentation = mask.merge(mask.frPyObjects(segmentation, *canvas_size))
+        else:
+            raise ValueError(f"COCO segmentation expected to be a dict or a list, got {type(segmentation)}")
         return torch.from_numpy(mask.decode(segmentation))
 
     def wrapper(idx, sample):