improve StrEnum (#5512)

* improve StrEnum

* use StrEnum for model weights

* fix test

* migrate StrEnum to main area

test/test_prototype_transforms.py

@@ -126,7 +126,7 @@ class TestSmoke:
             (
                 transforms.Normalize(mean=[0.0, 0.0, 0.0], std=[1.0, 1.0, 1.0]),
                 itertools.chain.from_iterable(
-                    fn(color_spaces=["rgb"], dtypes=[torch.float32])
+                    fn(color_spaces=[features.ColorSpace.RGB], dtypes=[torch.float32])
                     for fn in [
                         make_images,
                         make_vanilla_tensor_images,

test/test_prototype_transforms_functional.py

@@ -14,8 +14,6 @@ make_tensor = functools.partial(torch.testing.make_tensor, device="cpu")
 def make_image(size=None, *, color_space, extra_dims=(), dtype=torch.float32):
     size = size or torch.randint(16, 33, (2,)).tolist()
 
-    if isinstance(color_space, str):
-        color_space = features.ColorSpace[color_space]
     num_channels = {
         features.ColorSpace.GRAYSCALE: 1,
         features.ColorSpace.RGB: 3,

torchvision/_utils.py

+import enum
+
+
+class StrEnumMeta(enum.EnumMeta):
+    auto = enum.auto
+
+    def from_str(self, member: str):
+        try:
+            return self[member]
+        except KeyError:
+            # TODO: use `add_suggestion` from torchvision.prototype.utils._internal to improve the error message as
+            #  soon as it is migrated.
+            raise ValueError(f"Unknown value '{member}' for {self.__name__}.") from None
+
+
+class StrEnum(enum.Enum, metaclass=StrEnumMeta):
+    pass

torchvision/prototype/features/_bounding_box.py

@@ -3,7 +3,7 @@ from __future__ import annotations
 from typing import Any, Tuple, Union, Optional
 
 import torch
-from torchvision.prototype.utils._internal import StrEnum
+from torchvision._utils import StrEnum
 
 from ._feature import _Feature
 
@@ -30,7 +30,7 @@ class BoundingBox(_Feature):
         bounding_box = super().__new__(cls, data, dtype=dtype, device=device)
 
         if isinstance(format, str):
-            format = BoundingBoxFormat[format]
+            format = BoundingBoxFormat.from_str(format.upper())
 
         bounding_box._metadata.update(dict(format=format, image_size=image_size))
 

torchvision/prototype/features/_image.py

@@ -4,7 +4,7 @@ import warnings
 from typing import Any, Optional, Union, Tuple, cast
 
 import torch
-from torchvision.prototype.utils._internal import StrEnum
+from torchvision._utils import StrEnum
 from torchvision.transforms.functional import to_pil_image
 from torchvision.utils import draw_bounding_boxes
 from torchvision.utils import make_grid
@@ -14,9 +14,9 @@ from ._feature import _Feature
 
 
 class ColorSpace(StrEnum):
-    OTHER = 0
-    GRAYSCALE = 1
-    RGB = 3
+    OTHER = StrEnum.auto()
+    GRAYSCALE = StrEnum.auto()
+    RGB = StrEnum.auto()
 
 
 class Image(_Feature):
@@ -37,7 +37,7 @@ class Image(_Feature):
             if color_space == ColorSpace.OTHER:
                 warnings.warn("Unable to guess a specific color space. Consider passing it explicitly.")
         elif isinstance(color_space, str):
-            color_space = ColorSpace[color_space]
+            color_space = ColorSpace.from_str(color_space.upper())
 
         image._metadata.update(dict(color_space=color_space))
 

torchvision/prototype/models/_api.py

@@ -3,9 +3,10 @@ import inspect
 import sys
 from collections import OrderedDict
 from dataclasses import dataclass, fields
-from enum import Enum
 from typing import Any, Callable, Dict
 
+from torchvision._utils import StrEnum
+
 from ..._internally_replaced_utils import load_state_dict_from_url
 
 
@@ -34,7 +35,7 @@ class Weights:
     meta: Dict[str, Any]
 
 
-class WeightsEnum(Enum):
+class WeightsEnum(StrEnum):
     """
     This class is the parent class of all model weights. Each model building method receives an optional `weights`
     parameter with its associated pre-trained weights. It inherits from `Enum` and its values should be of type
@@ -58,12 +59,6 @@ class WeightsEnum(Enum):
                 )
         return obj
 
-    @classmethod
-    def from_str(cls, value: str) -> "WeightsEnum":
-        if value in cls.__members__:
-            return cls.__members__[value]
-        raise ValueError(f"Invalid value {value} for enum {cls.__name__}.")
-
     def get_state_dict(self, progress: bool) -> OrderedDict:
         return load_state_dict_from_url(self.url, progress=progress)
 

torchvision/prototype/utils/_internal.py

 import collections.abc
 import difflib
-import enum
 import functools
 import inspect
 import io
@@ -31,7 +30,6 @@ import numpy as np
 import torch
 
 __all__ = [
-    "StrEnum",
     "sequence_to_str",
     "add_suggestion",
     "FrozenMapping",
@@ -45,17 +43,6 @@ __all__ = [
 ]
 
 
-class StrEnumMeta(enum.EnumMeta):
-    auto = enum.auto
-
-    def __getitem__(self, item):
-        return super().__getitem__(item.upper() if isinstance(item, str) else item)
-
-
-class StrEnum(enum.Enum, metaclass=StrEnumMeta):
-    pass
-
-
 def sequence_to_str(seq: Sequence, separate_last: str = "") -> str:
     if not seq:
         return ""