add support for instance checks on dataset wrappers (#7239)

references/detection/coco_utils.py

@@ -178,9 +178,7 @@ def get_coco_api_from_dataset(dataset):
             break
         if isinstance(dataset, torch.utils.data.Subset):
             dataset = dataset.dataset
-    if isinstance(dataset, torchvision.datasets.CocoDetection) or isinstance(
-        getattr(dataset, "_dataset", None), torchvision.datasets.CocoDetection
-    ):
+    if isinstance(dataset, torchvision.datasets.CocoDetection):
         return dataset.coco
     return convert_to_coco_api(dataset)
 

references/detection/group_by_aspect_ratio.py

@@ -164,9 +164,7 @@ def compute_aspect_ratios(dataset, indices=None):
     if hasattr(dataset, "get_height_and_width"):
         return _compute_aspect_ratios_custom_dataset(dataset, indices)
 
-    if isinstance(dataset, torchvision.datasets.CocoDetection) or isinstance(
-        getattr(dataset, "_dataset", None), torchvision.datasets.CocoDetection
-    ):
+    if isinstance(dataset, torchvision.datasets.CocoDetection):
         return _compute_aspect_ratios_coco_dataset(dataset, indices)
 
     if isinstance(dataset, torchvision.datasets.VOCDetection):

test/datasets_utils.py

@@ -571,7 +571,7 @@ class DatasetTestCase(unittest.TestCase):
         from torchvision.datasets import wrap_dataset_for_transforms_v2
 
         try:
-            with self.create_dataset(config) as (dataset, _):
+            with self.create_dataset(config) as (dataset, info):
                 for target_keys in [None, "all"]:
                     if target_keys is not None and self.DATASET_CLASS not in {
                         torchvision.datasets.CocoDetection,
@@ -584,8 +584,10 @@ class DatasetTestCase(unittest.TestCase):
                         continue
 
                     wrapped_dataset = wrap_dataset_for_transforms_v2(dataset, target_keys=target_keys)
-                    wrapped_sample = wrapped_dataset[0]
+                    assert isinstance(wrapped_dataset, self.DATASET_CLASS)
+                    assert len(wrapped_dataset) == info["num_examples"]
 
+                    wrapped_sample = wrapped_dataset[0]
                     assert tree_any(lambda item: isinstance(item, (Datapoint, PIL.Image.Image)), wrapped_sample)
         except TypeError as error:
             msg = f"No wrapper exists for dataset class {type(dataset).__name__}"

torchvision/datapoints/_dataset_wrapper.py

@@ -8,7 +8,6 @@ import contextlib
 from collections import defaultdict
 
 import torch
-from torch.utils.data import Dataset
 
 from torchvision import datapoints, datasets
 from torchvision.transforms.v2 import functional as F
@@ -98,7 +97,16 @@ def wrap_dataset_for_transforms_v2(dataset, target_keys=None):
             f"but got {target_keys}"
         )
 
-    return VisionDatasetDatapointWrapper(dataset, target_keys)
+    # Imagine we have isinstance(dataset, datasets.ImageNet). This will create a new class with the name
+    # "WrappedImageNet" at runtime that doubly inherits from VisionDatasetDatapointWrapper (see below) as well as the
+    # original ImageNet class. This allows the user to do regular isinstance(wrapped_dataset, datasets.ImageNet) checks,
+    # while we can still inject everything that we need.
+    wrapped_dataset_cls = type(f"Wrapped{type(dataset).__name__}", (VisionDatasetDatapointWrapper, type(dataset)), {})
+    # Since VisionDatasetDatapointWrapper comes before ImageNet in the MRO, calling the class hits
+    # VisionDatasetDatapointWrapper.__init__ first. Since we are never doing super().__init__(...), the constructor of
+    # ImageNet is never hit. That is by design, since we don't want to create the dataset instance again, but rather
+    # have the existing instance as attribute on the new object.
+    return wrapped_dataset_cls(dataset, target_keys)
 
 
 class WrapperFactories(dict):
@@ -117,7 +125,7 @@ class WrapperFactories(dict):
 WRAPPER_FACTORIES = WrapperFactories()
 
 
-class VisionDatasetDatapointWrapper(Dataset):
+class VisionDatasetDatapointWrapper:
     def __init__(self, dataset, target_keys):
         dataset_cls = type(dataset)