streamline v2 check

test/datasets_utils.py

@@ -3,7 +3,6 @@
 import importlib
 import inspect
 import itertools
-import multiprocessing
 import os
 import pathlib
 import random
@@ -180,27 +179,30 @@ def check_transforms_v2_wrapper(dataset_test_case, *, config=None, supports_targ
     from torchvision import datapoints
     from torchvision.datasets import wrap_dataset_for_transforms_v2
 
+    def check_wrapped_samples(dataset):
+        for wrapped_sample in dataset:
+            assert tree_any(
+                lambda item: isinstance(item, (datapoints.Image, datapoints.Video, PIL.Image.Image)), wrapped_sample
+            )
+
     target_keyss = [None]
     if supports_target_keys:
         target_keyss.append("all")
 
-    for target_keys, multiprocessing_context in itertools.product(
-        target_keyss, multiprocessing.get_all_start_methods()
-    ):
+    for target_keys in target_keyss:
         with dataset_test_case.create_dataset(config) as (dataset, info):
             wrapped_dataset = wrap_dataset_for_transforms_v2(dataset, target_keys=target_keys)
 
             assert isinstance(wrapped_dataset, type(dataset))
             assert len(wrapped_dataset) == info["num_examples"]
 
-            dataloader = DataLoader(
-                wrapped_dataset, num_workers=2, multiprocessing_context=multiprocessing_context, collate_fn=_no_collate
-            )
+            check_wrapped_samples(wrapped_dataset)
 
-            for wrapped_sample in dataloader:
-                assert tree_any(
-                    lambda item: isinstance(item, (datapoints.Image, datapoints.Video, PIL.Image.Image)), wrapped_sample
-                )
+    with dataset_test_case.create_dataset(config) as (dataset, _):
+        wrapped_dataset = wrap_dataset_for_transforms_v2(dataset)
+        dataloader = DataLoader(wrapped_dataset, num_workers=2, multiprocessing_context="spawn", collate_fn=_no_collate)
+
+        check_wrapped_samples(dataloader)
 
 
 class DatasetTestCase(unittest.TestCase):