2023-08-24 nightly release (054432d)

test/common_extended_utils.py

@@ -140,6 +140,12 @@ def conv_backward_flop(inputs: List[Any], outputs: List[Any]):
     return flop_count
 
 
+def scaled_dot_product_flash_attention_flop(inputs: List[Any], outputs: List[Any]):
+    # FIXME: this needs to count the flops of this kernel
+    # https://github.com/pytorch/pytorch/blob/207b06d099def9d9476176a1842e88636c1f714f/aten/src/ATen/native/cpu/FlashAttentionKernel.cpp#L52-L267
+    return 0
+
+
 flop_mapping = {
     aten.mm: matmul_flop,
     aten.matmul: matmul_flop,
@@ -150,6 +156,7 @@ def conv_backward_flop(inputs: List[Any], outputs: List[Any]):
     aten.convolution_backward: conv_backward_flop,
     quantized.conv2d: quant_conv_flop,
     quantized.conv2d_relu: quant_conv_flop,
+    aten._scaled_dot_product_flash_attention: scaled_dot_product_flash_attention_flop,
 }
 
 unmapped_ops = set()

test/datasets_utils.py

@@ -5,6 +5,7 @@
 import itertools
 import os
 import pathlib
+import platform
 import random
 import shutil
 import string
@@ -548,7 +549,7 @@ def test_feature_types(self, config):
     @test_all_configs
     def test_num_examples(self, config):
         with self.create_dataset(config) as (dataset, info):
-            assert len(dataset) == info["num_examples"]
+            assert len(list(dataset)) == len(dataset) == info["num_examples"]
 
     @test_all_configs
     def test_transforms(self, config):
@@ -692,6 +693,31 @@ def test_transforms_v2_wrapper(self, config):
         super().test_transforms_v2_wrapper.__wrapped__(self, config)
 
 
+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.")
+
+    from torch.utils.data import DataLoader
+    from torchvision import datapoints
+    from torchvision.datasets import wrap_dataset_for_transforms_v2
+
+    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, (datapoints.Image, datapoints.Video, PIL.Image.Image)), wrapped_sample
+        )
+
+
 def create_image_or_video_tensor(size: Sequence[int]) -> torch.Tensor:
     r"""Create a random uint8 tensor.
 

test/test_datasets.py

@@ -183,14 +183,18 @@ def test_combined_targets(self):
                 ), "Type of the combined target does not match the type of the corresponding individual target: "
                 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)
+
 
 class Caltech256TestCase(datasets_utils.ImageDatasetTestCase):
     DATASET_CLASS = datasets.Caltech256
 
     def inject_fake_data(self, tmpdir, config):
         tmpdir = pathlib.Path(tmpdir) / "caltech256" / "256_ObjectCategories"
 
-        categories = ((1, "ak47"), (127, "laptop-101"), (257, "clutter"))
+        categories = ((1, "ak47"), (2, "american-flag"), (3, "backpack"))
         num_images_per_category = 2
 
         for idx, category in categories:
@@ -258,6 +262,10 @@ 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)
+
 
 class CityScapesTestCase(datasets_utils.ImageDatasetTestCase):
     DATASET_CLASS = datasets.Cityscapes
@@ -382,6 +390,11 @@ def test_feature_types_target_polygon(self):
             assert isinstance(polygon_img, PIL.Image.Image)
             (polygon_target, info["expected_polygon_target"])
 
+    def test_transforms_v2_wrapper_spawn(self):
+        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)
+
 
 class ImageNetTestCase(datasets_utils.ImageDatasetTestCase):
     DATASET_CLASS = datasets.ImageNet
@@ -413,6 +426,10 @@ def inject_fake_data(self, tmpdir, config):
         torch.save((wnid_to_classes, None), tmpdir / "meta.bin")
         return num_examples
 
+    def test_transforms_v2_wrapper_spawn(self):
+        with self.create_dataset() as (dataset, _):
+            datasets_utils.check_transforms_v2_wrapper_spawn(dataset)
+
 
 class CIFAR10TestCase(datasets_utils.ImageDatasetTestCase):
     DATASET_CLASS = datasets.CIFAR10
@@ -607,6 +624,11 @@ def test_images_names_split(self):
 
         assert merged_imgs_names == all_imgs_names
 
+    def test_transforms_v2_wrapper_spawn(self):
+        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)
+
 
 class VOCSegmentationTestCase(datasets_utils.ImageDatasetTestCase):
     DATASET_CLASS = datasets.VOCSegmentation
@@ -694,6 +716,10 @@ 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)
+
 
 class VOCDetectionTestCase(VOCSegmentationTestCase):
     DATASET_CLASS = datasets.VOCDetection
@@ -714,6 +740,10 @@ 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)
+
 
 class CocoDetectionTestCase(datasets_utils.ImageDatasetTestCase):
     DATASET_CLASS = datasets.CocoDetection
@@ -784,6 +814,10 @@ def _create_json(self, root, name, content):
             json.dump(content, fh)
         return file
 
+    def test_transforms_v2_wrapper_spawn(self):
+        with self.create_dataset() as (dataset, _):
+            datasets_utils.check_transforms_v2_wrapper_spawn(dataset)
+
 
 class CocoCaptionsTestCase(CocoDetectionTestCase):
     DATASET_CLASS = datasets.CocoCaptions
@@ -800,6 +834,11 @@ def test_captions(self):
             _, captions = dataset[0]
             assert tuple(captions) == tuple(info["captions"])
 
+    def test_transforms_v2_wrapper_spawn(self):
+        # We need to define this method, because otherwise the test from the super class will
+        # be run
+        pytest.skip("CocoCaptions is currently not supported by the v2 wrapper.")
+
 
 class UCF101TestCase(datasets_utils.VideoDatasetTestCase):
     DATASET_CLASS = datasets.UCF101
@@ -966,6 +1005,10 @@ def inject_fake_data(self, tmpdir, config):
             )
         return num_videos_per_class * len(classes)
 
+    def test_transforms_v2_wrapper_spawn(self):
+        with self.create_dataset(output_format="TCHW") as (dataset, _):
+            datasets_utils.check_transforms_v2_wrapper_spawn(dataset)
+
 
 class HMDB51TestCase(datasets_utils.VideoDatasetTestCase):
     DATASET_CLASS = datasets.HMDB51
@@ -1193,6 +1236,10 @@ def _create_segmentation(self, size):
     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)
+
 
 class FakeDataTestCase(datasets_utils.ImageDatasetTestCase):
     DATASET_CLASS = datasets.FakeData
@@ -1642,6 +1689,10 @@ 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)
+
 
 class SvhnTestCase(datasets_utils.ImageDatasetTestCase):
     DATASET_CLASS = datasets.SVHN
@@ -2516,6 +2567,10 @@ def _meta_to_split_and_classification_ann(self, meta, idx):
         breed_id = "-1"
         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)
+
 
 class StanfordCarsTestCase(datasets_utils.ImageDatasetTestCase):
     DATASET_CLASS = datasets.StanfordCars

test/test_extended_models.py

@@ -242,7 +242,6 @@ def test_naming_conventions(model_fn):
 )
 @run_if_test_with_extended
 def test_schema_meta_validation(model_fn):
-
     if model_fn.__name__ == "maskrcnn_resnet50_fpn_v2":
         pytest.skip(reason="FIXME https://github.com/pytorch/vision/issues/7349")
 
@@ -326,9 +325,11 @@ def test_schema_meta_validation(model_fn):
                     height, width = detection_models_input_dims[model_name]
                     kwargs = {"height": height, "width": width}
 
-                calculated_ops = get_ops(model=model, weight=w, **kwargs)
-                if calculated_ops != w.meta["_ops"]:
-                    incorrect_meta.append((w, "_ops"))
+                if not model_fn.__name__.startswith("vit"):
+                    # FIXME: https://github.com/pytorch/vision/issues/7871
+                    calculated_ops = get_ops(model=model, weight=w, **kwargs)
+                    if calculated_ops != w.meta["_ops"]:
+                        incorrect_meta.append((w, "_ops"))
 
         if not w.name.isupper():
             bad_names.append(w)

test/test_transforms_v2.py

@@ -1,5 +1,6 @@
 import itertools
 import pathlib
+import pickle
 import random
 import warnings
 
@@ -169,8 +170,11 @@ class TestSmoke:
             next(make_vanilla_tensor_images()),
         ],
     )
+    @pytest.mark.parametrize("de_serialize", [lambda t: t, lambda t: pickle.loads(pickle.dumps(t))])
     @pytest.mark.parametrize("device", cpu_and_cuda())
-    def test_common(self, transform, adapter, container_type, image_or_video, device):
+    def test_common(self, transform, adapter, container_type, image_or_video, de_serialize, device):
+        transform = de_serialize(transform)
+
         canvas_size = F.get_size(image_or_video)
         input = dict(
             image_or_video=image_or_video,

test/test_transforms_v2_refactored.py

@@ -2,6 +2,7 @@
 import decimal
 import inspect
 import math
+import pickle
 import re
 from pathlib import Path
 from unittest import mock
@@ -247,6 +248,8 @@ def _check_transform_v1_compatibility(transform, input):
 def check_transform(transform_cls, input, *args, **kwargs):
     transform = transform_cls(*args, **kwargs)
 
+    pickle.loads(pickle.dumps(transform))
+
     output = transform(input)
     assert isinstance(output, type(input))
 

torchvision/datapoints/_dataset_wrapper.py

@@ -162,6 +162,7 @@ def __init__(self, dataset, target_keys):
                 raise TypeError(msg)
 
         self._dataset = dataset
+        self._target_keys = target_keys
         self._wrapper = wrapper_factory(dataset, target_keys)
 
         # We need to disable the transforms on the dataset here to be able to inject the wrapping before we apply them.
@@ -197,6 +198,9 @@ def __getitem__(self, idx):
     def __len__(self):
         return len(self._dataset)
 
+    def __reduce__(self):
+        return wrap_dataset_for_transforms_v2, (self._dataset, self._target_keys)
+
 
 def raise_not_supported(description):
     raise RuntimeError(

torchvision/datasets/widerface.py

@@ -137,13 +137,13 @@ def parse_train_val_annotations_file(self) -> None:
                             {
                                 "img_path": img_path,
                                 "annotations": {
-                                    "bbox": labels_tensor[:, 0:4],  # x, y, width, height
-                                    "blur": labels_tensor[:, 4],
-                                    "expression": labels_tensor[:, 5],
-                                    "illumination": labels_tensor[:, 6],
-                                    "occlusion": labels_tensor[:, 7],
-                                    "pose": labels_tensor[:, 8],
-                                    "invalid": labels_tensor[:, 9],
+                                    "bbox": labels_tensor[:, 0:4].clone(),  # x, y, width, height
+                                    "blur": labels_tensor[:, 4].clone(),
+                                    "expression": labels_tensor[:, 5].clone(),
+                                    "illumination": labels_tensor[:, 6].clone(),
+                                    "occlusion": labels_tensor[:, 7].clone(),
+                                    "pose": labels_tensor[:, 8].clone(),
+                                    "invalid": labels_tensor[:, 9].clone(),
                                 },
                             }
                         )