[proto] Added mid-level ops and feature-based ops

.github/workflows/prototype-tests.yml

@@ -41,4 +41,4 @@ jobs:
 
       - name: Run prototype tests
         shell: bash
-        run: pytest --durations=20 test/test_prototype_*.py
+        run: pytest -vvv --durations=20 test/test_prototype_*.py

test/test_prototype_transforms_functional.py

@@ -365,6 +365,18 @@ def rotate_segmentation_mask():
         )
 
 
+@register_kernel_info_from_sample_inputs_fn
+def crop_image_tensor():
+    for image, top, left, height, width in itertools.product(make_images(), [-8, 0, 9], [-8, 0, 9], [12, 20], [12, 20]):
+        yield SampleInput(
+            image,
+            top=top,
+            left=left,
+            height=height,
+            width=width,
+        )
+
+
 @register_kernel_info_from_sample_inputs_fn
 def crop_bounding_box():
     for bounding_box, top, left in itertools.product(make_bounding_boxes(), [-8, 0, 9], [-8, 0, 9]):
@@ -414,6 +426,17 @@ def resized_crop_segmentation_mask():
         yield SampleInput(mask, top=top, left=left, height=height, width=width, size=size)
 
 
+@register_kernel_info_from_sample_inputs_fn
+def pad_image_tensor():
+    for image, padding, fill, padding_mode in itertools.product(
+        make_images(),
+        [[1], [1, 1], [1, 1, 2, 2]],  # padding
+        [12, 12.0],  # fill
+        ["constant", "symmetric", "edge", "reflect"],  # padding mode,
+    ):
+        yield SampleInput(image, padding=padding, fill=fill, padding_mode=padding_mode)
+
+
 @register_kernel_info_from_sample_inputs_fn
 def pad_segmentation_mask():
     for mask, padding, padding_mode in itertools.product(
@@ -499,6 +522,39 @@ def test_scriptable(kernel):
     jit.script(kernel)
 
 
+# Test below is intended to test mid-level op vs low-level ops it calls
+# For example, resize -> resize_image_tensor, resize_bounding_boxes etc
+# TODO: Rewrite this tests as sample args may include more or less params
+# than needed by functions
+@pytest.mark.parametrize(
+    "func",
+    [
+        pytest.param(func, id=name)
+        for name, func in F.__dict__.items()
+        if not name.startswith("_")
+        and callable(func)
+        and all(
+            feature_type not in name for feature_type in {"image", "segmentation_mask", "bounding_box", "label", "pil"}
+        )
+        and name not in {"to_image_tensor", "InterpolationMode", "decode_video_with_av", "crop", "rotate"}
+        # We skip 'crop' due to missing 'height' and 'width'
+        # We skip 'rotate' due to non implemented yet expand=True case for bboxes
+    ],
+)
+def test_functional_mid_level(func):
+    finfos = [finfo for finfo in FUNCTIONAL_INFOS if f"{func.__name__}_" in finfo.name]
+    for finfo in finfos:
+        for sample_input in finfo.sample_inputs():
+            expected = finfo(sample_input)
+            kwargs = dict(sample_input.kwargs)
+            for key in ["format", "image_size"]:
+                if key in kwargs:
+                    del kwargs[key]
+            output = func(*sample_input.args, **kwargs)
+            torch.testing.assert_close(output, expected, msg=f"finfo={finfo}, output={output}, expected={expected}")
+            break
+
+
 @pytest.mark.parametrize(
     ("functional_info", "sample_input"),
     [

torchvision/prototype/features/_bounding_box.py

@@ -1,9 +1,10 @@
 from __future__ import annotations
 
-from typing import Any, Tuple, Union, Optional
+from typing import Any, List, Tuple, Union, Optional, Sequence
 
 import torch
 from torchvision._utils import StrEnum
+from torchvision.transforms import InterpolationMode
 
 from ._feature import _Feature
 
@@ -69,3 +70,142 @@ def to_format(self, format: Union[str, BoundingBoxFormat]) -> BoundingBox:
         return BoundingBox.new_like(
             self, convert_bounding_box_format(self, old_format=self.format, new_format=format), format=format
         )
+
+    def horizontal_flip(self) -> BoundingBox:
+        from torchvision.prototype.transforms import functional as _F
+
+        output = _F.horizontal_flip_bounding_box(self, format=self.format, image_size=self.image_size)
+        return BoundingBox.new_like(self, output)
+
+    def vertical_flip(self) -> BoundingBox:
+        from torchvision.prototype.transforms import functional as _F
+
+        output = _F.vertical_flip_bounding_box(self, format=self.format, image_size=self.image_size)
+        return BoundingBox.new_like(self, output)
+
+    def resize(  # type: ignore[override]
+        self,
+        size: List[int],
+        interpolation: InterpolationMode = InterpolationMode.BILINEAR,
+        max_size: Optional[int] = None,
+        antialias: bool = False,
+    ) -> BoundingBox:
+        from torchvision.prototype.transforms import functional as _F
+
+        output = _F.resize_bounding_box(self, size, image_size=self.image_size, max_size=max_size)
+        image_size = (size[0], size[0]) if len(size) == 1 else (size[0], size[1])
+        return BoundingBox.new_like(self, output, image_size=image_size, dtype=output.dtype)
+
+    def crop(self, top: int, left: int, height: int, width: int) -> BoundingBox:
+        from torchvision.prototype.transforms import functional as _F
+
+        output = _F.crop_bounding_box(self, self.format, top, left)
+        return BoundingBox.new_like(self, output, image_size=(height, width))
+
+    def center_crop(self, output_size: List[int]) -> BoundingBox:
+        from torchvision.prototype.transforms import functional as _F
+
+        output = _F.center_crop_bounding_box(
+            self, format=self.format, output_size=output_size, image_size=self.image_size
+        )
+        image_size = (output_size[0], output_size[0]) if len(output_size) == 1 else (output_size[0], output_size[1])
+        return BoundingBox.new_like(self, output, image_size=image_size)
+
+    def resized_crop(
+        self,
+        top: int,
+        left: int,
+        height: int,
+        width: int,
+        size: List[int],
+        interpolation: InterpolationMode = InterpolationMode.BILINEAR,
+        antialias: bool = False,
+    ) -> BoundingBox:
+        from torchvision.prototype.transforms import functional as _F
+
+        output = _F.resized_crop_bounding_box(self, self.format, top, left, height, width, size=size)
+        image_size = (size[0], size[0]) if len(size) == 1 else (size[0], size[1])
+        return BoundingBox.new_like(self, output, image_size=image_size, dtype=output.dtype)
+
+    def pad(
+        self, padding: List[int], fill: Union[int, float, Sequence[float]] = 0, padding_mode: str = "constant"
+    ) -> BoundingBox:
+        from torchvision.prototype.transforms import functional as _F
+
+        if padding_mode not in ["constant"]:
+            raise ValueError(f"Padding mode '{padding_mode}' is not supported with bounding boxes")
+
+        output = _F.pad_bounding_box(self, padding, format=self.format)
+
+        # Update output image size:
+        # TODO: remove the import below and make _parse_pad_padding available
+        from torchvision.transforms.functional_tensor import _parse_pad_padding
+
+        left, top, right, bottom = _parse_pad_padding(padding)
+        height, width = self.image_size
+        height += top + bottom
+        width += left + right
+
+        return BoundingBox.new_like(self, output, image_size=(height, width))
+
+    def rotate(
+        self,
+        angle: float,
+        interpolation: InterpolationMode = InterpolationMode.NEAREST,
+        expand: bool = False,
+        fill: Optional[List[float]] = None,
+        center: Optional[List[float]] = None,
+    ) -> BoundingBox:
+        from torchvision.prototype.transforms import functional as _F
+
+        output = _F.rotate_bounding_box(
+            self, format=self.format, image_size=self.image_size, angle=angle, expand=expand, center=center
+        )
+        # TODO: update output image size if expand is True
+        if expand:
+            raise RuntimeError("Not yet implemented")
+        return BoundingBox.new_like(self, output, dtype=output.dtype)
+
+    def affine(
+        self,
+        angle: float,
+        translate: List[float],
+        scale: float,
+        shear: List[float],
+        interpolation: InterpolationMode = InterpolationMode.NEAREST,
+        fill: Optional[List[float]] = None,
+        center: Optional[List[float]] = None,
+    ) -> BoundingBox:
+        from torchvision.prototype.transforms import functional as _F
+
+        output = _F.affine_bounding_box(
+            self,
+            self.format,
+            self.image_size,
+            angle,
+            translate=translate,
+            scale=scale,
+            shear=shear,
+            center=center,
+        )
+        return BoundingBox.new_like(self, output, dtype=output.dtype)
+
+    def perspective(
+        self,
+        perspective_coeffs: List[float],
+        interpolation: InterpolationMode = InterpolationMode.BILINEAR,
+        fill: Optional[List[float]] = None,
+    ) -> BoundingBox:
+        from torchvision.prototype.transforms import functional as _F
+
+        output = _F.perspective_bounding_box(self, self.format, perspective_coeffs)
+        return BoundingBox.new_like(self, output, dtype=output.dtype)
+
+    def erase(self, i: int, j: int, h: int, w: int, v: torch.Tensor) -> BoundingBox:
+        raise TypeError("Erase transformation does not support bounding boxes")
+
+    def mixup(self, lam: float) -> BoundingBox:
+        raise TypeError("Mixup transformation does not support bounding boxes")
+
+    def cutmix(self, box: Tuple[int, int, int, int], lam_adjusted: float) -> BoundingBox:
+        raise TypeError("Cutmix transformation does not support bounding boxes")

torchvision/prototype/features/_feature.py

@@ -1,8 +1,8 @@
-from typing import Any, cast, TypeVar, Union, Optional, Type, Callable, Tuple, Sequence, Mapping
+from typing import Any, cast, TypeVar, Union, Optional, Type, Callable, List, Tuple, Sequence, Mapping
 
 import torch
 from torch._C import _TensorBase, DisableTorchFunction
-
+from torchvision.transforms import InterpolationMode
 
 F = TypeVar("F", bound="_Feature")
 
@@ -83,3 +83,115 @@ def __torch_function__(
             return cls.new_like(args[0], output, dtype=output.dtype, device=output.device)
         else:
             return output
+
+    def horizontal_flip(self) -> Any:
+        return self
+
+    def vertical_flip(self) -> Any:
+        return self
+
+    # TODO: We have to ignore override mypy error as there is torch.Tensor built-in deprecated op: Tensor.resize
+    # https://github.com/pytorch/pytorch/blob/e8727994eb7cdb2ab642749d6549bc497563aa06/torch/_tensor.py#L588-L593
+    def resize(  # type: ignore[override]
+        self,
+        size: List[int],
+        interpolation: InterpolationMode = InterpolationMode.BILINEAR,
+        max_size: Optional[int] = None,
+        antialias: bool = False,
+    ) -> Any:
+        return self
+
+    def crop(self, top: int, left: int, height: int, width: int) -> Any:
+        return self
+
+    def center_crop(self, output_size: List[int]) -> Any:
+        return self
+
+    def resized_crop(
+        self,
+        top: int,
+        left: int,
+        height: int,
+        width: int,
+        size: List[int],
+        interpolation: InterpolationMode = InterpolationMode.BILINEAR,
+        antialias: bool = False,
+    ) -> Any:
+        return self
+
+    def pad(
+        self, padding: List[int], fill: Union[int, float, Sequence[float]] = 0, padding_mode: str = "constant"
+    ) -> Any:
+        return self
+
+    def rotate(
+        self,
+        angle: float,
+        interpolation: InterpolationMode = InterpolationMode.NEAREST,
+        expand: bool = False,
+        fill: Optional[List[float]] = None,
+        center: Optional[List[float]] = None,
+    ) -> Any:
+        return self
+
+    def affine(
+        self,
+        angle: float,
+        translate: List[float],
+        scale: float,
+        shear: List[float],
+        interpolation: InterpolationMode = InterpolationMode.NEAREST,
+        fill: Optional[List[float]] = None,
+        center: Optional[List[float]] = None,
+    ) -> Any:
+        return self
+
+    def perspective(
+        self,
+        perspective_coeffs: List[float],
+        interpolation: InterpolationMode = InterpolationMode.BILINEAR,
+        fill: Optional[List[float]] = None,
+    ) -> Any:
+        return self
+
+    def adjust_brightness(self, brightness_factor: float) -> Any:
+        return self
+
+    def adjust_saturation(self, saturation_factor: float) -> Any:
+        return self
+
+    def adjust_contrast(self, contrast_factor: float) -> Any:
+        return self
+
+    def adjust_sharpness(self, sharpness_factor: float) -> Any:
+        return self
+
+    def adjust_hue(self, hue_factor: float) -> Any:
+        return self
+
+    def adjust_gamma(self, gamma: float, gain: float = 1) -> Any:
+        return self
+
+    def posterize(self, bits: int) -> Any:
+        return self
+
+    def solarize(self, threshold: float) -> Any:
+        return self
+
+    def autocontrast(self) -> Any:
+        return self
+
+    def equalize(self) -> Any:
+        return self
+
+    def invert(self) -> Any:
+        return self
+
+    def erase(self, i: int, j: int, h: int, w: int, v: torch.Tensor) -> Any:
+        return self
+
+    def mixup(self, lam: float) -> Any:
+        return self
+
+    def cutmix(self, box: Tuple[int, int, int, int], lam_adjusted: float) -> Any:
+        return self