[prototype] Rewrite the meta dimension methods (#6722)

* Rewrite `get_dimensions`, `get_num_channels` and `get_spatial_size`

* Remove `get_chw`

* Remove comments

* Make `get_spatial_size` support non-image input

* Reduce the unnecessary use of `get_dimensions*`

* Fix linters

* Fix merge bug

* Linter

* Fix linter

Author: datumbox

torchvision/prototype/features/_mask.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import Any, List, Optional, Union
+from typing import Any, cast, List, Optional, Tuple, Union
 
 import torch
 from torchvision.transforms import InterpolationMode
@@ -32,6 +32,10 @@ def wrap_like(
     ) -> Mask:
         return cls._wrap(tensor)
 
+    @property
+    def image_size(self) -> Tuple[int, int]:
+        return cast(Tuple[int, int], tuple(self.shape[-2:]))
+
     def horizontal_flip(self) -> Mask:
         output = self._F.horizontal_flip_mask(self)
         return Mask.wrap_like(self, output)

torchvision/prototype/transforms/_auto_augment.py

@@ -7,7 +7,7 @@
 from torch.utils._pytree import tree_flatten, tree_unflatten
 from torchvision.prototype import features
 from torchvision.prototype.transforms import AutoAugmentPolicy, functional as F, InterpolationMode, Transform
-from torchvision.prototype.transforms.functional._meta import get_chw
+from torchvision.prototype.transforms.functional._meta import get_spatial_size
 
 from ._utils import _isinstance, _setup_fill_arg
 
@@ -278,7 +278,7 @@ def forward(self, *inputs: Any) -> Any:
         sample = inputs if len(inputs) > 1 else inputs[0]
 
         id, image_or_video = self._extract_image_or_video(sample)
-        _, height, width = get_chw(image_or_video)
+        height, width = get_spatial_size(image_or_video)
 
         policy = self._policies[int(torch.randint(len(self._policies), ()))]
 
@@ -349,7 +349,7 @@ def forward(self, *inputs: Any) -> Any:
         sample = inputs if len(inputs) > 1 else inputs[0]
 
         id, image_or_video = self._extract_image_or_video(sample)
-        _, height, width = get_chw(image_or_video)
+        height, width = get_spatial_size(image_or_video)
 
         for _ in range(self.num_ops):
             transform_id, (magnitudes_fn, signed) = self._get_random_item(self._AUGMENTATION_SPACE)
@@ -403,7 +403,7 @@ def forward(self, *inputs: Any) -> Any:
         sample = inputs if len(inputs) > 1 else inputs[0]
 
         id, image_or_video = self._extract_image_or_video(sample)
-        _, height, width = get_chw(image_or_video)
+        height, width = get_spatial_size(image_or_video)
 
         transform_id, (magnitudes_fn, signed) = self._get_random_item(self._AUGMENTATION_SPACE)
 
@@ -473,7 +473,7 @@ def _sample_dirichlet(self, params: torch.Tensor) -> torch.Tensor:
     def forward(self, *inputs: Any) -> Any:
         sample = inputs if len(inputs) > 1 else inputs[0]
         id, orig_image_or_video = self._extract_image_or_video(sample)
-        _, height, width = get_chw(orig_image_or_video)
+        height, width = get_spatial_size(orig_image_or_video)
 
         if isinstance(orig_image_or_video, torch.Tensor):
             image_or_video = orig_image_or_video

torchvision/prototype/transforms/_utils.py

@@ -10,7 +10,7 @@
 from torchvision.prototype import features
 from torchvision.prototype.features._feature import FillType
 
-from torchvision.prototype.transforms.functional._meta import get_chw
+from torchvision.prototype.transforms.functional._meta import get_dimensions
 from torchvision.transforms.transforms import _check_sequence_input, _setup_angle, _setup_size  # noqa: F401
 
 from typing_extensions import Literal
@@ -80,15 +80,16 @@ def query_bounding_box(sample: Any) -> features.BoundingBox:
 def query_chw(sample: Any) -> Tuple[int, int, int]:
     flat_sample, _ = tree_flatten(sample)
     chws = {
-        get_chw(item)
+        tuple(get_dimensions(item))
         for item in flat_sample
         if isinstance(item, (features.Image, PIL.Image.Image, features.Video)) or features.is_simple_tensor(item)
     }
     if not chws:
         raise TypeError("No image or video was found in the sample")
     elif len(chws) > 1:
         raise ValueError(f"Found multiple CxHxW dimensions in the sample: {sequence_to_str(sorted(chws))}")
-    return chws.pop()
+    c, h, w = chws.pop()
+    return c, h, w
 
 
 def _isinstance(obj: Any, types_or_checks: Tuple[Union[Type, Callable[[Any], bool]], ...]) -> bool:

torchvision/prototype/transforms/functional/__init__.py

@@ -8,9 +8,15 @@
     convert_color_space_image_pil,
     convert_color_space_video,
     convert_color_space,
+    get_dimensions_image_tensor,
+    get_dimensions_image_pil,
     get_dimensions,
     get_image_num_channels,
+    get_num_channels_image_tensor,
+    get_num_channels_image_pil,
     get_num_channels,
+    get_spatial_size_image_tensor,
+    get_spatial_size_image_pil,
     get_spatial_size,
 )  # usort: skip
 

torchvision/prototype/transforms/functional/_geometry.py

@@ -21,7 +21,12 @@
     interpolate,
 )
 
-from ._meta import convert_format_bounding_box, get_dimensions_image_pil, get_dimensions_image_tensor
+from ._meta import (
+    convert_format_bounding_box,
+    get_dimensions_image_tensor,
+    get_spatial_size_image_pil,
+    get_spatial_size_image_tensor,
+)
 
 horizontal_flip_image_tensor = _FT.hflip
 horizontal_flip_image_pil = _FP.hflip
@@ -323,7 +328,7 @@ def affine_image_pil(
     # it is visually better to estimate the center without 0.5 offset
     # otherwise image rotated by 90 degrees is shifted vs output image of torch.rot90 or F_t.affine
     if center is None:
-        _, height, width = get_dimensions_image_pil(image)
+        height, width = get_spatial_size_image_pil(image)
         center = [width * 0.5, height * 0.5]
     matrix = _get_inverse_affine_matrix(center, angle, translate, scale, shear)
 
@@ -1189,13 +1194,13 @@ def _center_crop_compute_crop_anchor(
 
 def center_crop_image_tensor(image: torch.Tensor, output_size: List[int]) -> torch.Tensor:
     crop_height, crop_width = _center_crop_parse_output_size(output_size)
-    _, image_height, image_width = get_dimensions_image_tensor(image)
+    image_height, image_width = get_spatial_size_image_tensor(image)
 
     if crop_height > image_height or crop_width > image_width:
         padding_ltrb = _center_crop_compute_padding(crop_height, crop_width, image_height, image_width)
         image = pad_image_tensor(image, padding_ltrb, fill=0)
 
-        _, image_height, image_width = get_dimensions_image_tensor(image)
+        image_height, image_width = get_spatial_size_image_tensor(image)
         if crop_width == image_width and crop_height == image_height:
             return image
 
@@ -1206,13 +1211,13 @@ def center_crop_image_tensor(image: torch.Tensor, output_size: List[int]) -> tor
 @torch.jit.unused
 def center_crop_image_pil(image: PIL.Image.Image, output_size: List[int]) -> PIL.Image.Image:
     crop_height, crop_width = _center_crop_parse_output_size(output_size)
-    _, image_height, image_width = get_dimensions_image_pil(image)
+    image_height, image_width = get_spatial_size_image_pil(image)
 
     if crop_height > image_height or crop_width > image_width:
         padding_ltrb = _center_crop_compute_padding(crop_height, crop_width, image_height, image_width)
         image = pad_image_pil(image, padding_ltrb, fill=0)
 
-        _, image_height, image_width = get_dimensions_image_pil(image)
+        image_height, image_width = get_spatial_size_image_pil(image)
         if crop_width == image_width and crop_height == image_height:
             return image
 
@@ -1365,7 +1370,7 @@ def five_crop_image_tensor(
     image: torch.Tensor, size: List[int]
 ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
     crop_height, crop_width = _parse_five_crop_size(size)
-    _, image_height, image_width = get_dimensions_image_tensor(image)
+    image_height, image_width = get_spatial_size_image_tensor(image)
 
     if crop_width > image_width or crop_height > image_height:
         msg = "Requested crop size {} is bigger than input size {}"
@@ -1385,7 +1390,7 @@ def five_crop_image_pil(
     image: PIL.Image.Image, size: List[int]
 ) -> Tuple[PIL.Image.Image, PIL.Image.Image, PIL.Image.Image, PIL.Image.Image, PIL.Image.Image]:
     crop_height, crop_width = _parse_five_crop_size(size)
-    _, image_height, image_width = get_dimensions_image_pil(image)
+    image_height, image_width = get_spatial_size_image_pil(image)
 
     if crop_width > image_width or crop_height > image_height:
         msg = "Requested crop size {} is bigger than input size {}"

torchvision/prototype/transforms/functional/_meta.py

@@ -6,48 +6,66 @@
 from torchvision.prototype.features import BoundingBoxFormat, ColorSpace
 from torchvision.transforms import functional_pil as _FP, functional_tensor as _FT
 
+
 get_dimensions_image_tensor = _FT.get_dimensions
 get_dimensions_image_pil = _FP.get_dimensions
 
 
-# TODO: Should this be prefixed with `_` similar to other methods that don't get exposed by init?
-def get_chw(image: features.ImageOrVideoTypeJIT) -> Tuple[int, int, int]:
+def get_dimensions(image: features.ImageOrVideoTypeJIT) -> List[int]:
     if isinstance(image, torch.Tensor) and (
         torch.jit.is_scripting() or not isinstance(image, (features.Image, features.Video))
     ):
-        channels, height, width = get_dimensions_image_tensor(image)
+        return get_dimensions_image_tensor(image)
     elif isinstance(image, (features.Image, features.Video)):
         channels = image.num_channels
         height, width = image.image_size
-    else:  # isinstance(image, PIL.Image.Image)
-        channels, height, width = get_dimensions_image_pil(image)
-    return channels, height, width
-
-
-# The three functions below are here for BC. Whether we want to have two different kernels and how they and the
-# compound version should be named is still under discussion: https://github.com/pytorch/vision/issues/6491
-# Given that these kernels should also support boxes, masks, and videos, it is unlikely that there name will stay.
-# They will either be deprecated or simply aliased to the new kernels if we have reached consensus about the issue
-# detailed above.
+        return [channels, height, width]
+    else:
+        return get_dimensions_image_pil(image)
 
 
-def get_dimensions(image: features.ImageOrVideoTypeJIT) -> List[int]:
-    return list(get_chw(image))
+get_num_channels_image_tensor = _FT.get_image_num_channels
+get_num_channels_image_pil = _FP.get_image_num_channels
 
 
 def get_num_channels(image: features.ImageOrVideoTypeJIT) -> int:
-    num_channels, *_ = get_chw(image)
-    return num_channels
+    if isinstance(image, torch.Tensor) and (
+        torch.jit.is_scripting() or not isinstance(image, (features.Image, features.Video))
+    ):
+        return _FT.get_image_num_channels(image)
+    elif isinstance(image, (features.Image, features.Video)):
+        return image.num_channels
+    else:
+        return _FP.get_image_num_channels(image)
 
 
 # We changed the names to ensure it can be used not only for images but also videos. Thus, we just alias it without
 # deprecating the old names.
 get_image_num_channels = get_num_channels
 
 
-def get_spatial_size(image: features.ImageOrVideoTypeJIT) -> List[int]:
-    _, *size = get_chw(image)
-    return size
+def get_spatial_size_image_tensor(image: torch.Tensor) -> List[int]:
+    width, height = _FT.get_image_size(image)
+    return [height, width]
+
+
+@torch.jit.unused
+def get_spatial_size_image_pil(image: PIL.Image.Image) -> List[int]:
+    width, height = _FP.get_image_size(image)
+    return [height, width]
+
+
+def get_spatial_size(inpt: features.InputTypeJIT) -> List[int]:
+    if isinstance(inpt, torch.Tensor) and (torch.jit.is_scripting() or not isinstance(inpt, features._Feature)):
+        return get_spatial_size_image_tensor(inpt)
+    elif isinstance(inpt, features._Feature):
+        image_size = getattr(inpt, "image_size", None)
+        if image_size is not None:
+            return list(image_size)
+        else:
+            raise ValueError(f"Type {inpt.__class__} doesn't have spatial size.")
+    else:
+        return get_spatial_size_image_pil(inpt)
 
 
 def _xywh_to_xyxy(xywh: torch.Tensor) -> torch.Tensor: