[proto] Fixed fill type in AA

torchvision/prototype/transforms/_auto_augment.py

@@ -1,6 +1,5 @@
 import math
-import numbers
-from typing import Any, Callable, cast, Dict, List, Optional, Sequence, Tuple, Type, TypeVar, Union
+from typing import Any, Callable, cast, Dict, List, Optional, Tuple, Type, TypeVar, Union
 
 import PIL.Image
 import torch
@@ -10,7 +9,7 @@
 from torchvision.prototype.transforms import AutoAugmentPolicy, functional as F, InterpolationMode, Transform
 from torchvision.prototype.transforms.functional._meta import get_chw
 
-from ._utils import _isinstance
+from ._utils import _isinstance, _setup_fill_arg, FillType
 
 K = TypeVar("K")
 V = TypeVar("V")
@@ -21,14 +20,11 @@ def __init__(
         self,
         *,
         interpolation: InterpolationMode = InterpolationMode.NEAREST,
-        fill: Union[int, float, Sequence[int], Sequence[float]] = 0,
+        fill: Union[FillType, Dict[Type, FillType]] = 0,
     ) -> None:
         super().__init__()
         self.interpolation = interpolation
-
-        if not isinstance(fill, (numbers.Number, tuple, list)):
-            raise TypeError("Got inappropriate fill arg")
-        self.fill = fill
+        self.fill = _setup_fill_arg(fill)
 
     def _get_random_item(self, dct: Dict[K, V]) -> Tuple[K, V]:
         keys = tuple(dct.keys())
@@ -63,18 +59,19 @@ def _put_into_sample(self, sample: Any, id: int, item: Any) -> Any:
 
     def _apply_image_transform(
         self,
-        image: Any,
+        image: Union[torch.Tensor, PIL.Image.Image, features.Image],
         transform_id: str,
         magnitude: float,
         interpolation: InterpolationMode,
-        fill: Union[int, float, Sequence[int], Sequence[float]],
+        fill: Dict[Type, FillType],
     ) -> Any:
 
+        fill_ = fill[type(image)]
         # Fill = 0 is not equivalent to None, https://github.com/pytorch/vision/issues/6517
         # So, we have to put fill as None if fill == 0
         # This is due to BC with stable API which has fill = None by default
-        fill_ = F._geometry._convert_fill_arg(fill)
-        if isinstance(fill, int) and fill == 0:
+        fill_ = F._geometry._convert_fill_arg(fill_)
+        if isinstance(fill_, int) and fill_ == 0:
             fill_ = None
 
         if transform_id == "Identity":
@@ -186,7 +183,7 @@ def __init__(
         self,
         policy: AutoAugmentPolicy = AutoAugmentPolicy.IMAGENET,
         interpolation: InterpolationMode = InterpolationMode.NEAREST,
-        fill: Union[int, float, Sequence[int], Sequence[float]] = 0,
+        fill: Union[FillType, Dict[Type, FillType]] = 0,
     ) -> None:
         super().__init__(interpolation=interpolation, fill=fill)
         self.policy = policy
@@ -286,7 +283,7 @@ def forward(self, *inputs: Any) -> Any:
         sample = inputs if len(inputs) > 1 else inputs[0]
 
         id, image = self._extract_image(sample)
-        num_channels, height, width = get_chw(image)
+        _, height, width = get_chw(image)
 
         policy = self._policies[int(torch.randint(len(self._policies), ()))]
 
@@ -346,7 +343,7 @@ def __init__(
         magnitude: int = 9,
         num_magnitude_bins: int = 31,
         interpolation: InterpolationMode = InterpolationMode.NEAREST,
-        fill: Union[int, float, Sequence[int], Sequence[float]] = 0,
+        fill: Union[FillType, Dict[Type, FillType]] = 0,
     ) -> None:
         super().__init__(interpolation=interpolation, fill=fill)
         self.num_ops = num_ops
@@ -402,7 +399,7 @@ def __init__(
         self,
         num_magnitude_bins: int = 31,
         interpolation: InterpolationMode = InterpolationMode.NEAREST,
-        fill: Union[int, float, Sequence[int], Sequence[float]] = 0,
+        fill: Union[FillType, Dict[Type, FillType]] = 0,
     ):
         super().__init__(interpolation=interpolation, fill=fill)
         self.num_magnitude_bins = num_magnitude_bins
@@ -462,7 +459,7 @@ def __init__(
         alpha: float = 1.0,
         all_ops: bool = True,
         interpolation: InterpolationMode = InterpolationMode.BILINEAR,
-        fill: Union[int, float, Sequence[int], Sequence[float]] = 0,
+        fill: Union[FillType, Dict[Type, FillType]] = 0,
     ) -> None:
         super().__init__(interpolation=interpolation, fill=fill)
         self._PARAMETER_MAX = 10

torchvision/prototype/transforms/_deprecated.py

@@ -11,10 +11,7 @@
 from typing_extensions import Literal
 
 from ._transform import _RandomApplyTransform
-from ._utils import query_chw
-
-
-DType = Union[torch.Tensor, PIL.Image.Image, features._Feature]
+from ._utils import DType, query_chw
 
 
 class ToTensor(Transform):

torchvision/prototype/transforms/_geometry.py

@@ -1,7 +1,6 @@
 import math
 import numbers
 import warnings
-from collections import defaultdict
 from typing import Any, cast, Dict, List, Optional, Sequence, Tuple, Type, Union
 
 import PIL.Image
@@ -14,11 +13,20 @@
 from typing_extensions import Literal
 
 from ._transform import _RandomApplyTransform
-from ._utils import _check_sequence_input, _setup_angle, _setup_size, has_all, has_any, query_bounding_box, query_chw
-
-
-DType = Union[torch.Tensor, PIL.Image.Image, features._Feature]
-FillType = Union[int, float, Sequence[int], Sequence[float]]
+from ._utils import (
+    _check_padding_arg,
+    _check_padding_mode_arg,
+    _check_sequence_input,
+    _setup_angle,
+    _setup_fill_arg,
+    _setup_size,
+    DType,
+    FillType,
+    has_all,
+    has_any,
+    query_bounding_box,
+    query_chw,
+)
 
 
 class RandomHorizontalFlip(_RandomApplyTransform):
@@ -201,40 +209,6 @@ def forward(self, *inputs: Any) -> Any:
         return super().forward(*inputs)
 
 
-def _check_fill_arg(fill: Union[FillType, Dict[Type, FillType]]) -> None:
-    if isinstance(fill, dict):
-        for key, value in fill.items():
-            # Check key for type
-            _check_fill_arg(value)
-    else:
-        if not isinstance(fill, (numbers.Number, tuple, list)):
-            raise TypeError("Got inappropriate fill arg")
-
-
-def _setup_fill_arg(fill: Union[FillType, Dict[Type, FillType]]) -> Dict[Type, FillType]:
-    _check_fill_arg(fill)
-
-    if isinstance(fill, dict):
-        return fill
-
-    return defaultdict(lambda: fill)  # type: ignore[arg-type, return-value]
-
-
-def _check_padding_arg(padding: Union[int, Sequence[int]]) -> None:
-    if not isinstance(padding, (numbers.Number, tuple, list)):
-        raise TypeError("Got inappropriate padding arg")
-
-    if isinstance(padding, (tuple, list)) and len(padding) not in [1, 2, 4]:
-        raise ValueError(f"Padding must be an int or a 1, 2, or 4 element tuple, not a {len(padding)} element tuple")
-
-
-# TODO: let's use torchvision._utils.StrEnum to have the best of both worlds (strings and enums)
-# https://github.com/pytorch/vision/issues/6250
-def _check_padding_mode_arg(padding_mode: Literal["constant", "edge", "reflect", "symmetric"]) -> None:
-    if padding_mode not in ["constant", "edge", "reflect", "symmetric"]:
-        raise ValueError("Padding mode should be either constant, edge, reflect or symmetric")
-
-
 class Pad(Transform):
     def __init__(
         self,

torchvision/prototype/transforms/_utils.py

@@ -1,13 +1,59 @@
-from typing import Any, Callable, Tuple, Type, Union
+import numbers
+from collections import defaultdict
+
+from typing import Any, Callable, Dict, Sequence, Tuple, Type, Union
 
 import PIL.Image
+
+import torch
 from torch.utils._pytree import tree_flatten
 from torchvision._utils import sequence_to_str
 from torchvision.prototype import features
 
 from torchvision.prototype.transforms.functional._meta import get_chw
 from torchvision.transforms.transforms import _check_sequence_input, _setup_angle, _setup_size  # noqa: F401
 
+from typing_extensions import Literal
+
+
+# Type shortcuts:
+DType = Union[torch.Tensor, PIL.Image.Image, features._Feature]
+FillType = Union[int, float, Sequence[int], Sequence[float]]
+
+
+def _check_fill_arg(fill: Union[FillType, Dict[Type, FillType]]) -> None:
+    if isinstance(fill, dict):
+        for key, value in fill.items():
+            # Check key for type
+            _check_fill_arg(value)
+    else:
+        if not isinstance(fill, (numbers.Number, tuple, list)):
+            raise TypeError("Got inappropriate fill arg")
+
+
+def _setup_fill_arg(fill: Union[FillType, Dict[Type, FillType]]) -> Dict[Type, FillType]:
+    _check_fill_arg(fill)
+
+    if isinstance(fill, dict):
+        return fill
+
+    return defaultdict(lambda: fill)  # type: ignore[arg-type, return-value]
+
+
+def _check_padding_arg(padding: Union[int, Sequence[int]]) -> None:
+    if not isinstance(padding, (numbers.Number, tuple, list)):
+        raise TypeError("Got inappropriate padding arg")
+
+    if isinstance(padding, (tuple, list)) and len(padding) not in [1, 2, 4]:
+        raise ValueError(f"Padding must be an int or a 1, 2, or 4 element tuple, not a {len(padding)} element tuple")
+
+
+# TODO: let's use torchvision._utils.StrEnum to have the best of both worlds (strings and enums)
+# https://github.com/pytorch/vision/issues/6250
+def _check_padding_mode_arg(padding_mode: Literal["constant", "edge", "reflect", "symmetric"]) -> None:
+    if padding_mode not in ["constant", "edge", "reflect", "symmetric"]:
+        raise ValueError("Padding mode should be either constant, edge, reflect or symmetric")
+
 
 def query_bounding_box(sample: Any) -> features.BoundingBox:
     flat_sample, _ = tree_flatten(sample)