More cleanup for prototype transforms

torchvision/prototype/transforms/__init__.py

@@ -1,9 +1,11 @@
+from torchvision.transforms import AutoAugmentPolicy, InterpolationMode  # usort: skip
+
 from . import functional  # usort: skip
 
 from ._transform import Transform  # usort: skip
 
 from ._augment import RandomCutmix, RandomErasing, RandomMixup, SimpleCopyPaste
-from ._auto_augment import AugMix, AutoAugment, AutoAugmentPolicy, RandAugment, TrivialAugmentWide
+from ._auto_augment import AugMix, AutoAugment, RandAugment, TrivialAugmentWide
 from ._color import (
     ColorJitter,
     RandomAdjustSharpness,

torchvision/prototype/transforms/_augment.py

@@ -8,9 +8,7 @@
 from torch.utils._pytree import tree_flatten, tree_unflatten
 from torchvision.ops import masks_to_boxes
 from torchvision.prototype import features
-
-from torchvision.prototype.transforms import functional as F
-from torchvision.transforms.functional import InterpolationMode, pil_to_tensor
+from torchvision.prototype.transforms import functional as F, InterpolationMode
 
 from ._transform import _RandomApplyTransform
 from ._utils import has_any, is_simple_tensor, query_chw
@@ -278,7 +276,7 @@ def _extract_image_targets(self, flat_sample: List[Any]) -> Tuple[List[Any], Lis
             if isinstance(obj, features.Image) or is_simple_tensor(obj):
                 images.append(obj)
             elif isinstance(obj, PIL.Image.Image):
-                images.append(pil_to_tensor(obj))
+                images.append(F.to_image_tensor(obj))
             elif isinstance(obj, features.BoundingBox):
                 bboxes.append(obj)
             elif isinstance(obj, features.SegmentationMask):

torchvision/prototype/transforms/_auto_augment.py

@@ -7,9 +7,7 @@
 
 from torch.utils._pytree import tree_flatten, tree_unflatten
 from torchvision.prototype import features
-from torchvision.prototype.transforms import functional as F, Transform
-from torchvision.transforms.autoaugment import AutoAugmentPolicy
-from torchvision.transforms.functional import InterpolationMode, pil_to_tensor, to_pil_image
+from torchvision.prototype.transforms import AutoAugmentPolicy, functional as F, InterpolationMode, Transform
 
 from ._utils import _isinstance, get_chw, is_simple_tensor
 
@@ -473,7 +471,7 @@ def forward(self, *inputs: Any) -> Any:
         if isinstance(orig_image, torch.Tensor):
             image = orig_image
         else:  # isinstance(inpt, PIL.Image.Image):
-            image = pil_to_tensor(orig_image)
+            image = F.to_image_tensor(orig_image)
 
         augmentation_space = self._AUGMENTATION_SPACE if self.all_ops else self._PARTIAL_AUGMENTATION_SPACE
 
@@ -516,6 +514,6 @@ def forward(self, *inputs: Any) -> Any:
         if isinstance(orig_image, features.Image):
             mix = features.Image.new_like(orig_image, mix)
         elif isinstance(orig_image, PIL.Image.Image):
-            mix = to_pil_image(mix)
+            mix = F.to_image_pil(mix)
 
         return self._put_into_sample(sample, id, mix)

torchvision/prototype/transforms/_color.py

@@ -5,7 +5,6 @@
 import torch
 from torchvision.prototype import features
 from torchvision.prototype.transforms import functional as F, Transform
-from torchvision.transforms import functional as _F
 
 from ._transform import _RandomApplyTransform
 from ._utils import is_simple_tensor, query_chw
@@ -117,14 +116,14 @@ def _permute_channels(self, inpt: Any, *, permutation: torch.Tensor) -> Any:
 
         image = inpt
         if isinstance(inpt, PIL.Image.Image):
-            image = _F.pil_to_tensor(image)
+            image = F.to_image_tensor(image)
 
         output = image[..., permutation, :, :]
 
         if isinstance(inpt, features.Image):
             output = features.Image.new_like(inpt, output, color_space=features.ColorSpace.OTHER)
         elif isinstance(inpt, PIL.Image.Image):
-            output = _F.to_pil_image(output)
+            output = F.to_image_pil(output)
 
         return output
 

torchvision/prototype/transforms/_deprecated.py

@@ -18,6 +18,7 @@ class ToTensor(Transform):
     _transformed_types = (PIL.Image.Image, np.ndarray)
 
     def __init__(self) -> None:
+        # FIXME: should the replacement be a `Compose` with `ConvertImageDtype`?
         warnings.warn(
             "The transform `ToTensor()` is deprecated and will be removed in a future release. "
             "Instead, please use `transforms.ToImageTensor()`."

torchvision/prototype/transforms/_geometry.py

@@ -7,15 +7,22 @@
 import torch
 from torchvision.ops.boxes import box_iou
 from torchvision.prototype import features
-from torchvision.prototype.transforms import functional as F, Transform
-from torchvision.transforms.functional import InterpolationMode
-from torchvision.transforms.functional_tensor import _parse_pad_padding
-from torchvision.transforms.transforms import _check_sequence_input, _setup_angle, _setup_size
+from torchvision.prototype.transforms import functional as F, InterpolationMode, Transform
 
 from typing_extensions import Literal
 
 from ._transform import _RandomApplyTransform
-from ._utils import has_all, has_any, is_simple_tensor, query_bounding_box, query_chw
+from ._utils import (
+    _check_sequence_input,
+    _parse_pad_padding,
+    _setup_angle,
+    _setup_size,
+    has_all,
+    has_any,
+    is_simple_tensor,
+    query_bounding_box,
+    query_chw,
+)
 
 
 class RandomHorizontalFlip(_RandomApplyTransform):

torchvision/prototype/transforms/_meta.py

@@ -5,7 +5,6 @@
 import torch
 from torchvision.prototype import features
 from torchvision.prototype.transforms import functional as F, Transform
-from torchvision.transforms.functional import convert_image_dtype
 
 from ._utils import is_simple_tensor
 
@@ -32,7 +31,7 @@ def __init__(self, dtype: torch.dtype = torch.float32) -> None:
         self.dtype = dtype
 
     def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
-        output = convert_image_dtype(inpt, dtype=self.dtype)
+        output = F.convert_image_dtype(inpt, dtype=self.dtype)
         return output if is_simple_tensor(inpt) else features.Image.new_like(inpt, output, dtype=self.dtype)
 
 

torchvision/prototype/transforms/_misc.py

@@ -8,9 +8,8 @@
 from torchvision.prototype import features
 from torchvision.prototype.transforms import functional as F, Transform
 from torchvision.prototype.transforms._utils import query_bounding_box
-from torchvision.transforms.transforms import _setup_size
 
-from ._utils import is_simple_tensor
+from ._utils import _setup_size, is_simple_tensor
 
 
 class Identity(Transform):

torchvision/prototype/transforms/_type_conversion.py

@@ -14,6 +14,7 @@ class DecodeImage(Transform):
     _transformed_types = (features.EncodedImage,)
 
     def _transform(self, inpt: Any, params: Dict[str, Any]) -> features.Image:
+        # FIXME: should we move this wrapping into the functional?
         output = F.decode_image_with_pil(inpt)
         return features.Image(output)
 
@@ -43,6 +44,7 @@ class ToImageTensor(Transform):
     _transformed_types = (is_simple_tensor, PIL.Image.Image, np.ndarray)
 
     def _transform(self, inpt: Any, params: Dict[str, Any]) -> features.Image:
+        # FIXME: should we move this wrapping into the functional?
         output = F.to_image_tensor(inpt)
         return features.Image(output)
 

torchvision/prototype/transforms/_utils.py

@@ -5,6 +5,8 @@
 from torch.utils._pytree import tree_flatten
 from torchvision._utils import sequence_to_str
 from torchvision.prototype import features
+from torchvision.transforms.functional_tensor import _parse_pad_padding  # noqa: F401
+from torchvision.transforms.transforms import _check_sequence_input, _setup_angle, _setup_size  # noqa: F401
 
 from .functional._meta import get_dimensions_image_pil, get_dimensions_image_tensor
 

torchvision/prototype/transforms/functional/__init__.py

@@ -68,6 +68,7 @@
     five_crop,
     five_crop_image_pil,
     five_crop_image_tensor,
+    hflip,
     horizontal_flip,
     horizontal_flip_bounding_box,
     horizontal_flip_image_pil,
@@ -106,8 +107,17 @@
     vertical_flip_image_pil,
     vertical_flip_image_tensor,
     vertical_flip_segmentation_mask,
+    vflip,
 )
 from ._misc import gaussian_blur, gaussian_blur_image_pil, gaussian_blur_image_tensor, normalize, normalize_image_tensor
-from ._type_conversion import decode_image_with_pil, decode_video_with_av, to_image_pil, to_image_tensor
+from ._type_conversion import (
+    convert_image_dtype,
+    decode_image_with_pil,
+    decode_video_with_av,
+    pil_to_tensor,
+    to_image_pil,
+    to_image_tensor,
+    to_pil_image,
+)
 
 from ._deprecated import rgb_to_grayscale, to_grayscale  # usort: skip

torchvision/prototype/transforms/functional/_deprecated.py

@@ -2,6 +2,7 @@
 from typing import Any
 
 import PIL.Image
+import torch
 
 from torchvision.prototype import features
 from torchvision.transforms import functional as _F
@@ -41,3 +42,12 @@ def rgb_to_grayscale(inpt: Any, num_output_channels: int = 1) -> Any:
     )
 
     return _F.rgb_to_grayscale(inpt, num_output_channels=num_output_channels)
+
+
+def to_tensor(inpt: Any) -> torch.Tensor:
+    # FIXME: should we keep the "if needed" phrase or unconditionally recommend `convert_image_dtype`?
+    warnings.warn(
+        "The function `to_tensor(...)` is deprecated and will be removed in a future release. "
+        "Instead, please use `to_image_tensor(...)` and if needed use `convert_image_dtype(...)` afterwards."
+    )
+    return _F.to_tensor(inpt)

torchvision/prototype/transforms/functional/_geometry.py

@@ -89,6 +89,12 @@ def vertical_flip(inpt: DType) -> DType:
         return vertical_flip_image_tensor(inpt)
 
 
+# We changed the names to align them with the transforms, i.e. `RandomHorizontalFlip`. Still, `hflip` and `vflip` are
+# prevalent and well understood. Thus, we just alias them without deprecating the old names.
+hflip = horizontal_flip
+vflip = vertical_flip
+
+
 def resize_image_tensor(
     image: torch.Tensor,
     size: List[int],

torchvision/prototype/transforms/functional/_type_conversion.py

@@ -29,3 +29,10 @@ def to_image_tensor(image: Union[torch.Tensor, PIL.Image.Image, np.ndarray]) ->
 
 
 to_image_pil = _F.to_pil_image
+
+# We changed the names to align them with the new naming scheme. Still, `to_pil_image` and `pil_to_tensor` are
+# prevalent and well understood. Thus, we just alias them without deprecating the old names.
+to_pil_image = to_image_pil
+pil_to_tensor = to_image_tensor
+
+convert_image_dtype = _F.convert_image_dtype