[Cherry-pick] Fix v2 transforms in spawn mp context (#8067)

test/datasets_utils.py

@@ -27,7 +27,11 @@
 import torchvision.io
 from common_utils import disable_console_output, get_tmp_dir
 from torch.utils._pytree import tree_any
+from torch.utils.data import DataLoader
+from torchvision import tv_tensors
+from torchvision.datasets import wrap_dataset_for_transforms_v2
 from torchvision.transforms.functional import get_dimensions
+from torchvision.transforms.v2.functional import get_size
 
 
 __all__ = [
@@ -568,9 +572,6 @@ def test_transforms(self, config):
 
     @test_all_configs
     def test_transforms_v2_wrapper(self, config):
-        from torchvision import tv_tensors
-        from torchvision.datasets import wrap_dataset_for_transforms_v2
-
         try:
             with self.create_dataset(config) as (dataset, info):
                 for target_keys in [None, "all"]:
@@ -709,26 +710,29 @@ def _no_collate(batch):
     return batch
 
 
-def check_transforms_v2_wrapper_spawn(dataset):
-    # On Linux and Windows, the DataLoader forks the main process by default. This is not available on macOS, so new
-    # subprocesses are spawned. This requires the whole pipeline including the dataset to be pickleable, which is what
-    # we are enforcing here.
-    if platform.system() != "Darwin":
-        pytest.skip("Multiprocessing spawning is only checked on macOS.")
+def check_transforms_v2_wrapper_spawn(dataset, expected_size):
+    # This check ensures that the wrapped datasets can be used with multiprocessing_context="spawn" in the DataLoader.
+    # We also check that transforms are applied correctly as a non-regression test for
+    # https://github.com/pytorch/vision/issues/8066
+    # Implicitly, this also checks that the wrapped datasets are pickleable.
 
-    from torch.utils.data import DataLoader
-    from torchvision import tv_tensors
-    from torchvision.datasets import wrap_dataset_for_transforms_v2
+    # To save CI/test time, we only check on Windows where "spawn" is the default
+    if platform.system() != "Windows":
+        pytest.skip("Multiprocessing spawning is only checked on macOS.")
 
     wrapped_dataset = wrap_dataset_for_transforms_v2(dataset)
 
     dataloader = DataLoader(wrapped_dataset, num_workers=2, multiprocessing_context="spawn", collate_fn=_no_collate)
 
-    for wrapped_sample in dataloader:
-        assert tree_any(
-            lambda item: isinstance(item, (tv_tensors.Image, tv_tensors.Video, PIL.Image.Image)), wrapped_sample
+    def resize_was_applied(item):
+        # Checking the size of the output ensures that the Resize transform was correctly applied
+        return isinstance(item, (tv_tensors.Image, tv_tensors.Video, PIL.Image.Image)) and get_size(item) == list(
+            expected_size
         )
 
+    for wrapped_sample in dataloader:
+        assert tree_any(resize_was_applied, wrapped_sample)
+
 
 def create_image_or_video_tensor(size: Sequence[int]) -> torch.Tensor:
     r"""Create a random uint8 tensor.

test/test_datasets.py

@@ -24,6 +24,7 @@
 import torch.nn.functional as F
 from common_utils import combinations_grid
 from torchvision import datasets
+from torchvision.transforms import v2
 
 
 class STL10TestCase(datasets_utils.ImageDatasetTestCase):
@@ -184,8 +185,9 @@ def test_combined_targets(self):
                 f"{actual} is not {expected}",
 
     def test_transforms_v2_wrapper_spawn(self):
-        with self.create_dataset(target_type="category") as (dataset, _):
-            datasets_utils.check_transforms_v2_wrapper_spawn(dataset)
+        expected_size = (123, 321)
+        with self.create_dataset(target_type="category", transform=v2.Resize(size=expected_size)) as (dataset, _):
+            datasets_utils.check_transforms_v2_wrapper_spawn(dataset, expected_size=expected_size)
 
 
 class Caltech256TestCase(datasets_utils.ImageDatasetTestCase):
@@ -263,8 +265,9 @@ def inject_fake_data(self, tmpdir, config):
         return split_to_num_examples[config["split"]]
 
     def test_transforms_v2_wrapper_spawn(self):
-        with self.create_dataset() as (dataset, _):
-            datasets_utils.check_transforms_v2_wrapper_spawn(dataset)
+        expected_size = (123, 321)
+        with self.create_dataset(transform=v2.Resize(size=expected_size)) as (dataset, _):
+            datasets_utils.check_transforms_v2_wrapper_spawn(dataset, expected_size=expected_size)
 
 
 class CityScapesTestCase(datasets_utils.ImageDatasetTestCase):
@@ -391,9 +394,10 @@ def test_feature_types_target_polygon(self):
             (polygon_target, info["expected_polygon_target"])
 
     def test_transforms_v2_wrapper_spawn(self):
+        expected_size = (123, 321)
         for target_type in ["instance", "semantic", ["instance", "semantic"]]:
-            with self.create_dataset(target_type=target_type) as (dataset, _):
-                datasets_utils.check_transforms_v2_wrapper_spawn(dataset)
+            with self.create_dataset(target_type=target_type, transform=v2.Resize(size=expected_size)) as (dataset, _):
+                datasets_utils.check_transforms_v2_wrapper_spawn(dataset, expected_size=expected_size)
 
 
 class ImageNetTestCase(datasets_utils.ImageDatasetTestCase):
@@ -427,8 +431,9 @@ def inject_fake_data(self, tmpdir, config):
         return num_examples
 
     def test_transforms_v2_wrapper_spawn(self):
-        with self.create_dataset() as (dataset, _):
-            datasets_utils.check_transforms_v2_wrapper_spawn(dataset)
+        expected_size = (123, 321)
+        with self.create_dataset(transform=v2.Resize(size=expected_size)) as (dataset, _):
+            datasets_utils.check_transforms_v2_wrapper_spawn(dataset, expected_size=expected_size)
 
 
 class CIFAR10TestCase(datasets_utils.ImageDatasetTestCase):
@@ -625,9 +630,10 @@ def test_images_names_split(self):
         assert merged_imgs_names == all_imgs_names
 
     def test_transforms_v2_wrapper_spawn(self):
+        expected_size = (123, 321)
         for target_type in ["identity", "bbox", ["identity", "bbox"]]:
-            with self.create_dataset(target_type=target_type) as (dataset, _):
-                datasets_utils.check_transforms_v2_wrapper_spawn(dataset)
+            with self.create_dataset(target_type=target_type, transform=v2.Resize(size=expected_size)) as (dataset, _):
+                datasets_utils.check_transforms_v2_wrapper_spawn(dataset, expected_size=expected_size)
 
 
 class VOCSegmentationTestCase(datasets_utils.ImageDatasetTestCase):
@@ -717,8 +723,9 @@ def add_bndbox(obj, bndbox=None):
         return data
 
     def test_transforms_v2_wrapper_spawn(self):
-        with self.create_dataset() as (dataset, _):
-            datasets_utils.check_transforms_v2_wrapper_spawn(dataset)
+        expected_size = (123, 321)
+        with self.create_dataset(transform=v2.Resize(size=expected_size)) as (dataset, _):
+            datasets_utils.check_transforms_v2_wrapper_spawn(dataset, expected_size=expected_size)
 
 
 class VOCDetectionTestCase(VOCSegmentationTestCase):
@@ -741,8 +748,9 @@ def test_annotations(self):
             assert object == info["annotation"]
 
     def test_transforms_v2_wrapper_spawn(self):
-        with self.create_dataset() as (dataset, _):
-            datasets_utils.check_transforms_v2_wrapper_spawn(dataset)
+        expected_size = (123, 321)
+        with self.create_dataset(transform=v2.Resize(size=expected_size)) as (dataset, _):
+            datasets_utils.check_transforms_v2_wrapper_spawn(dataset, expected_size=expected_size)
 
 
 class CocoDetectionTestCase(datasets_utils.ImageDatasetTestCase):
@@ -815,8 +823,9 @@ def _create_json(self, root, name, content):
         return file
 
     def test_transforms_v2_wrapper_spawn(self):
-        with self.create_dataset() as (dataset, _):
-            datasets_utils.check_transforms_v2_wrapper_spawn(dataset)
+        expected_size = (123, 321)
+        with self.create_dataset(transform=v2.Resize(size=expected_size)) as (dataset, _):
+            datasets_utils.check_transforms_v2_wrapper_spawn(dataset, expected_size=expected_size)
 
 
 class CocoCaptionsTestCase(CocoDetectionTestCase):
@@ -1005,9 +1014,11 @@ def inject_fake_data(self, tmpdir, config):
             )
         return num_videos_per_class * len(classes)
 
+    @pytest.mark.xfail(reason="FIXME")
     def test_transforms_v2_wrapper_spawn(self):
-        with self.create_dataset(output_format="TCHW") as (dataset, _):
-            datasets_utils.check_transforms_v2_wrapper_spawn(dataset)
+        expected_size = (123, 321)
+        with self.create_dataset(output_format="TCHW", transform=v2.Resize(size=expected_size)) as (dataset, _):
+            datasets_utils.check_transforms_v2_wrapper_spawn(dataset, expected_size=expected_size)
 
 
 class HMDB51TestCase(datasets_utils.VideoDatasetTestCase):
@@ -1237,8 +1248,9 @@ def _file_stem(self, idx):
         return f"2008_{idx:06d}"
 
     def test_transforms_v2_wrapper_spawn(self):
-        with self.create_dataset(mode="segmentation") as (dataset, _):
-            datasets_utils.check_transforms_v2_wrapper_spawn(dataset)
+        expected_size = (123, 321)
+        with self.create_dataset(mode="segmentation", transforms=v2.Resize(size=expected_size)) as (dataset, _):
+            datasets_utils.check_transforms_v2_wrapper_spawn(dataset, expected_size=expected_size)
 
 
 class FakeDataTestCase(datasets_utils.ImageDatasetTestCase):
@@ -1690,8 +1702,9 @@ def inject_fake_data(self, tmpdir, config):
         return split_to_num_examples[config["train"]]
 
     def test_transforms_v2_wrapper_spawn(self):
-        with self.create_dataset() as (dataset, _):
-            datasets_utils.check_transforms_v2_wrapper_spawn(dataset)
+        expected_size = (123, 321)
+        with self.create_dataset(transform=v2.Resize(size=expected_size)) as (dataset, _):
+            datasets_utils.check_transforms_v2_wrapper_spawn(dataset, expected_size=expected_size)
 
 
 class SvhnTestCase(datasets_utils.ImageDatasetTestCase):
@@ -2568,8 +2581,9 @@ def _meta_to_split_and_classification_ann(self, meta, idx):
         return (image_id, class_id, species, breed_id)
 
     def test_transforms_v2_wrapper_spawn(self):
-        with self.create_dataset() as (dataset, _):
-            datasets_utils.check_transforms_v2_wrapper_spawn(dataset)
+        expected_size = (123, 321)
+        with self.create_dataset(transform=v2.Resize(size=expected_size)) as (dataset, _):
+            datasets_utils.check_transforms_v2_wrapper_spawn(dataset, expected_size=expected_size)
 
 
 class StanfordCarsTestCase(datasets_utils.ImageDatasetTestCase):

torchvision/tv_tensors/_dataset_wrapper.py

@@ -6,6 +6,7 @@
 
 import contextlib
 from collections import defaultdict
+from copy import copy
 
 import torch
 
@@ -198,8 +199,19 @@ def __getitem__(self, idx):
     def __len__(self):
         return len(self._dataset)
 
+    # TODO: maybe we should use __getstate__ and __setstate__ instead of __reduce__, as recommended in the docs.
     def __reduce__(self):
-        return wrap_dataset_for_transforms_v2, (self._dataset, self._target_keys)
+        # __reduce__ gets called when we try to pickle the dataset.
+        # In a DataLoader with spawn context, this gets called `num_workers` times from the main process.
+
+        # We have to reset the [target_]transform[s] attributes of the dataset
+        # to their original values, because we previously set them to None in __init__().
+        dataset = copy(self._dataset)
+        dataset.transform = self.transform
+        dataset.transforms = self.transforms
+        dataset.target_transform = self.target_transform
+
+        return wrap_dataset_for_transforms_v2, (dataset, self._target_keys)
 
 
 def raise_not_supported(description):