Merge branch 'main' into bugfix/coco-typecheck

test/test_datasets.py

@@ -1625,6 +1625,10 @@ def inject_fake_data(self, tmpdir, config):
             num_examples_total += num_examples
             classes.append(cls)
 
+        if config.pop("make_empty_class", False):
+            os.makedirs(pathlib.Path(tmpdir) / "empty_class")
+            classes.append("empty_class")
+
         return dict(num_examples=num_examples_total, classes=classes)
 
     def _file_name_fn(self, cls, ext, idx):
@@ -1649,6 +1653,23 @@ def test_classes(self, config):
             assert len(dataset.classes) == len(info["classes"])
             assert all([a == b for a, b in zip(dataset.classes, info["classes"])])
 
+    def test_allow_empty(self):
+        config = {
+            "extensions": self._EXTENSIONS,
+            "make_empty_class": True,
+        }
+
+        config["allow_empty"] = True
+        with self.create_dataset(config) as (dataset, info):
+            assert "empty_class" in dataset.classes
+            assert len(dataset.classes) == len(info["classes"])
+            assert all([a == b for a, b in zip(dataset.classes, info["classes"])])
+
+        config["allow_empty"] = False
+        with pytest.raises(FileNotFoundError, match="Found no valid file"):
+            with self.create_dataset(config) as (dataset, info):
+                pass
+
 
 class ImageFolderTestCase(datasets_utils.ImageDatasetTestCase):
     DATASET_CLASS = datasets.ImageFolder

test/test_transforms.py

@@ -1614,8 +1614,8 @@ def test_augmix(fill, severity, mixture_width, chain_depth, all_ops, grayscale):
 def test_random_crop():
     height = random.randint(10, 32) * 2
     width = random.randint(10, 32) * 2
-    oheight = random.randint(5, (height - 2) / 2) * 2
-    owidth = random.randint(5, (width - 2) / 2) * 2
+    oheight = random.randint(5, (height - 2) // 2) * 2
+    owidth = random.randint(5, (width - 2) // 2) * 2
     img = torch.ones(3, height, width, dtype=torch.uint8)
     result = transforms.Compose(
         [
@@ -1664,8 +1664,8 @@ def test_random_crop():
 def test_center_crop():
     height = random.randint(10, 32) * 2
     width = random.randint(10, 32) * 2
-    oheight = random.randint(5, (height - 2) / 2) * 2
-    owidth = random.randint(5, (width - 2) / 2) * 2
+    oheight = random.randint(5, (height - 2) // 2) * 2
+    owidth = random.randint(5, (width - 2) // 2) * 2
 
     img = torch.ones(3, height, width, dtype=torch.uint8)
     oh1 = (height - oheight) // 2

test/test_transforms_video.py

@@ -23,8 +23,8 @@ def test_random_crop_video(self):
         numFrames = random.randint(4, 128)
         height = random.randint(10, 32) * 2
         width = random.randint(10, 32) * 2
-        oheight = random.randint(5, (height - 2) / 2) * 2
-        owidth = random.randint(5, (width - 2) / 2) * 2
+        oheight = random.randint(5, (height - 2) // 2) * 2
+        owidth = random.randint(5, (width - 2) // 2) * 2
         clip = torch.randint(0, 256, (numFrames, height, width, 3), dtype=torch.uint8)
         result = Compose(
             [
@@ -41,8 +41,8 @@ def test_random_resized_crop_video(self):
         numFrames = random.randint(4, 128)
         height = random.randint(10, 32) * 2
         width = random.randint(10, 32) * 2
-        oheight = random.randint(5, (height - 2) / 2) * 2
-        owidth = random.randint(5, (width - 2) / 2) * 2
+        oheight = random.randint(5, (height - 2) // 2) * 2
+        owidth = random.randint(5, (width - 2) // 2) * 2
         clip = torch.randint(0, 256, (numFrames, height, width, 3), dtype=torch.uint8)
         result = Compose(
             [
@@ -59,8 +59,8 @@ def test_center_crop_video(self):
         numFrames = random.randint(4, 128)
         height = random.randint(10, 32) * 2
         width = random.randint(10, 32) * 2
-        oheight = random.randint(5, (height - 2) / 2) * 2
-        owidth = random.randint(5, (width - 2) / 2) * 2
+        oheight = random.randint(5, (height - 2) // 2) * 2
+        owidth = random.randint(5, (width - 2) // 2) * 2
 
         clip = torch.ones((numFrames, height, width, 3), dtype=torch.uint8) * 255
         oh1 = (height - oheight) // 2

torchvision/datasets/folder.py

@@ -50,6 +50,7 @@ def make_dataset(
     class_to_idx: Optional[Dict[str, int]] = None,
     extensions: Optional[Union[str, Tuple[str, ...]]] = None,
     is_valid_file: Optional[Callable[[str], bool]] = None,
+    allow_empty: bool = False,
 ) -> List[Tuple[str, int]]:
     """Generates a list of samples of a form (path_to_sample, class).
 
@@ -95,7 +96,7 @@ def is_valid_file(x: str) -> bool:
                         available_classes.add(target_class)
 
     empty_classes = set(class_to_idx.keys()) - available_classes
-    if empty_classes:
+    if empty_classes and not allow_empty:
         msg = f"Found no valid file for the classes {', '.join(sorted(empty_classes))}. "
         if extensions is not None:
             msg += f"Supported extensions are: {extensions if isinstance(extensions, str) else ', '.join(extensions)}"
@@ -123,6 +124,8 @@ class DatasetFolder(VisionDataset):
         is_valid_file (callable, optional): A function that takes path of a file
             and check if the file is a valid file (used to check of corrupt files)
             both extensions and is_valid_file should not be passed.
+        allow_empty(bool, optional): If True, empty folders are considered to be valid classes.
+            An error is raised on empty folders if False (default).
 
      Attributes:
         classes (list): List of the class names sorted alphabetically.
@@ -139,10 +142,17 @@ def __init__(
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
         is_valid_file: Optional[Callable[[str], bool]] = None,
+        allow_empty: bool = False,
     ) -> None:
         super().__init__(root, transform=transform, target_transform=target_transform)
         classes, class_to_idx = self.find_classes(self.root)
-        samples = self.make_dataset(self.root, class_to_idx, extensions, is_valid_file)
+        samples = self.make_dataset(
+            self.root,
+            class_to_idx=class_to_idx,
+            extensions=extensions,
+            is_valid_file=is_valid_file,
+            allow_empty=allow_empty,
+        )
 
         self.loader = loader
         self.extensions = extensions
@@ -158,6 +168,7 @@ def make_dataset(
         class_to_idx: Dict[str, int],
         extensions: Optional[Tuple[str, ...]] = None,
         is_valid_file: Optional[Callable[[str], bool]] = None,
+        allow_empty: bool = False,
     ) -> List[Tuple[str, int]]:
         """Generates a list of samples of a form (path_to_sample, class).
 
@@ -172,6 +183,8 @@ def make_dataset(
                 and checks if the file is a valid file
                 (used to check of corrupt files) both extensions and
                 is_valid_file should not be passed. Defaults to None.
+            allow_empty(bool, optional): If True, empty folders are considered to be valid classes.
+                An error is raised on empty folders if False (default).
 
         Raises:
             ValueError: In case ``class_to_idx`` is empty.
@@ -186,7 +199,9 @@ def make_dataset(
             # find_classes() function, instead of using that of the find_classes() method, which
             # is potentially overridden and thus could have a different logic.
             raise ValueError("The class_to_idx parameter cannot be None.")
-        return make_dataset(directory, class_to_idx, extensions=extensions, is_valid_file=is_valid_file)
+        return make_dataset(
+            directory, class_to_idx, extensions=extensions, is_valid_file=is_valid_file, allow_empty=allow_empty
+        )
 
     def find_classes(self, directory: str) -> Tuple[List[str], Dict[str, int]]:
         """Find the class folders in a dataset structured as follows::
@@ -291,6 +306,8 @@ class ImageFolder(DatasetFolder):
         loader (callable, optional): A function to load an image given its path.
         is_valid_file (callable, optional): A function that takes path of an Image file
             and check if the file is a valid file (used to check of corrupt files)
+        allow_empty(bool, optional): If True, empty folders are considered to be valid classes.
+            An error is raised on empty folders if False (default).
 
      Attributes:
         classes (list): List of the class names sorted alphabetically.
@@ -305,6 +322,7 @@ def __init__(
         target_transform: Optional[Callable] = None,
         loader: Callable[[str], Any] = default_loader,
         is_valid_file: Optional[Callable[[str], bool]] = None,
+        allow_empty: bool = False,
     ):
         super().__init__(
             root,
@@ -313,5 +331,6 @@ def __init__(
             transform=transform,
             target_transform=target_transform,
             is_valid_file=is_valid_file,
+            allow_empty=allow_empty,
         )
         self.imgs = self.samples

torchvision/models/maxvit.py

@@ -577,7 +577,7 @@ class MaxVit(nn.Module):
         stochastic_depth_prob (float): Probability of stochastic depth. Expands to a list of probabilities for each layer that scales linearly to the specified value.
         squeeze_ratio (float): Squeeze ratio in the SE Layer. Default: 0.25.
         expansion_ratio (float): Expansion ratio in the MBConv bottleneck. Default: 4.
-        norm_layer (Callable[..., nn.Module]): Normalization function. Default: None (setting to None will produce a `BatchNorm2d(eps=1e-3, momentum=0.99)`).
+        norm_layer (Callable[..., nn.Module]): Normalization function. Default: None (setting to None will produce a `BatchNorm2d(eps=1e-3, momentum=0.01)`).
         activation_layer (Callable[..., nn.Module]): Activation function Default: nn.GELU.
         head_dim (int): Dimension of the attention heads.
         mlp_ratio (int): Expansion ratio of the MLP layer. Default: 4.
@@ -623,7 +623,7 @@ def __init__(
         # https://github.com/google-research/maxvit/blob/da76cf0d8a6ec668cc31b399c4126186da7da944/maxvit/models/maxvit.py#L1029-L1030
         # for the exact parameters used in batchnorm
         if norm_layer is None:
-            norm_layer = partial(nn.BatchNorm2d, eps=1e-3, momentum=0.99)
+            norm_layer = partial(nn.BatchNorm2d, eps=1e-3, momentum=0.01)
 
         # Make sure input size will be divisible by the partition size in all blocks
         # Undefined behavior if H or W are not divisible by p
@@ -788,7 +788,8 @@ class MaxVit_T_Weights(WeightsEnum):
             },
             "_ops": 5.558,
             "_file_size": 118.769,
-            "_docs": """These weights reproduce closely the results of the paper using a similar training recipe.""",
+            "_docs": """These weights reproduce closely the results of the paper using a similar training recipe.
+            They were trained with a BatchNorm2D momentum of 0.99 instead of the more correct 0.01.""",
         },
     )
     DEFAULT = IMAGENET1K_V1