call dataset wrapper with idx and sample

torchvision/prototype/datapoints/_dataset_wrapper.py

@@ -74,7 +74,7 @@ def __getitem__(self, idx):
         # of this class
         sample = self._dataset[idx]
 
-        sample = self._wrapper(sample)
+        sample = self._wrapper(idx, sample)
 
         # Regardless of whether the user has supplied the transforms individually (`transform` and `target_transform`)
         # or joint (`transforms`), we can access the full functionality through `transforms`
@@ -125,7 +125,10 @@ def wrap_target_by_type(target, *, target_types, type_wrappers):
 
 
 def classification_wrapper_factory(dataset):
-    return identity
+    def wrapper(idx, sample):
+        return sample
+
+    return wrapper
 
 
 for dataset_cls in [
@@ -143,7 +146,7 @@ def classification_wrapper_factory(dataset):
 
 
 def segmentation_wrapper_factory(dataset):
-    def wrapper(sample):
+    def wrapper(idx, sample):
         image, mask = sample
         return image, pil_image_to_mask(mask)
 
@@ -163,7 +166,7 @@ def video_classification_wrapper_factory(dataset):
             f"since it is not compatible with the transformations. Please use `output_format='TCHW'` instead."
         )
 
-    def wrapper(sample):
+    def wrapper(idx, sample):
         video, audio, label = sample
 
         video = datapoints.Video(video)
@@ -201,14 +204,17 @@ def segmentation_to_mask(segmentation, *, spatial_size):
         )
         return torch.from_numpy(mask.decode(segmentation))
 
-    def wrapper(sample):
+    def wrapper(idx, sample):
+        image_id = dataset.ids[idx]
+
         image, target = sample
 
+        if not target:
+            return image, dict(image_id=image_id)
+
         batched_target = list_of_dicts_to_dict_of_lists(target)
 
-        image_ids = batched_target.pop("image_id")
-        image_id = batched_target["image_id"] = image_ids.pop()
-        assert all(other_image_id == image_id for other_image_id in image_ids)
+        batched_target["image_id"] = image_id
 
         spatial_size = tuple(F.get_spatial_size(image))
         batched_target["boxes"] = datapoints.BoundingBox(
@@ -259,7 +265,7 @@ def wrapper(sample):
 
 @WRAPPER_FACTORIES.register(datasets.VOCDetection)
 def voc_detection_wrapper_factory(dataset):
-    def wrapper(sample):
+    def wrapper(idx, sample):
         image, target = sample
 
         batched_instances = list_of_dicts_to_dict_of_lists(target["annotation"]["object"])
@@ -294,7 +300,7 @@ def celeba_wrapper_factory(dataset):
     if any(target_type in dataset.target_type for target_type in ["attr", "landmarks"]):
         raise_not_supported("`CelebA` dataset with `target_type=['attr', 'landmarks', ...]`")
 
-    def wrapper(sample):
+    def wrapper(idx, sample):
         image, target = sample
 
         target = wrap_target_by_type(
@@ -318,7 +324,7 @@ def wrapper(sample):
 
 @WRAPPER_FACTORIES.register(datasets.Kitti)
 def kitti_wrapper_factory(dataset):
-    def wrapper(sample):
+    def wrapper(idx, sample):
         image, target = sample
 
         if target is not None:
@@ -336,7 +342,7 @@ def wrapper(sample):
 
 @WRAPPER_FACTORIES.register(datasets.OxfordIIITPet)
 def oxford_iiit_pet_wrapper_factor(dataset):
-    def wrapper(sample):
+    def wrapper(idx, sample):
         image, target = sample
 
         if target is not None:
@@ -371,7 +377,7 @@ def instance_segmentation_wrapper(mask):
             labels.append(label)
         return dict(masks=datapoints.Mask(torch.stack(masks)), labels=torch.stack(labels))
 
-    def wrapper(sample):
+    def wrapper(idx, sample):
         image, target = sample
 
         target = wrap_target_by_type(
@@ -390,7 +396,7 @@ def wrapper(sample):
 
 @WRAPPER_FACTORIES.register(datasets.WIDERFace)
 def widerface_wrapper(dataset):
-    def wrapper(sample):
+    def wrapper(idx, sample):
         image, target = sample
 
         if target is not None: