[proto] Added some transformations and fixed type hints

test/test_functional_tensor.py

@@ -955,18 +955,7 @@ def test_adjust_gamma(device, dtype, config, channels):
 
 @pytest.mark.parametrize("device", cpu_and_gpu())
 @pytest.mark.parametrize("dt", [None, torch.float32, torch.float64, torch.float16])
-@pytest.mark.parametrize(
-    "pad",
-    [
-        2,
-        [
-            3,
-        ],
-        [0, 3],
-        (3, 3),
-        [4, 2, 4, 3],
-    ],
-)
+@pytest.mark.parametrize("pad", [2, [3], [0, 3], (3, 3), [4, 2, 4, 3]])
 @pytest.mark.parametrize(
     "config",
     [

test/test_prototype_transforms.py

@@ -3,7 +3,11 @@
 import pytest
 import torch
 from common_utils import assert_equal
-from test_prototype_transforms_functional import make_images, make_bounding_boxes, make_one_hot_labels
+from test_prototype_transforms_functional import (
+    make_images,
+    make_bounding_boxes,
+    make_one_hot_labels,
+)
 from torchvision.prototype import transforms, features
 from torchvision.transforms.functional import to_pil_image, pil_to_tensor
 
@@ -72,6 +76,9 @@ class TestSmoke:
         transforms.ConvertImageDtype(),
         transforms.RandomHorizontalFlip(),
         transforms.Pad(5),
+        transforms.RandomZoomOut(),
+        transforms.RandomRotation(degrees=(-45, 45)),
+        transforms.RandomAffine(degrees=(-45, 45)),
     )
     def test_common(self, transform, input):
         transform(input)

test/test_prototype_transforms_functional.py

@@ -317,7 +317,7 @@ def rotate_image_tensor():
         [-87, 15, 90],  # angle
         [True, False],  # expand
         [None, [12, 23]],  # center
-        [None, [128]],  # fill
+        [None, [128], [12.0]],  # fill
     ):
         if center is not None and expand:
             # Skip warning: The provided center argument is ignored if expand is True

torchvision/prototype/features/_bounding_box.py

@@ -128,13 +128,20 @@ def resized_crop(
         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"
+        self,
+        padding: Union[int, Sequence[int]],
+        fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
+        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")
 
+        # This cast does Sequence[int] -> List[int] and is required to make mypy happy
+        if not isinstance(padding, int):
+            padding = list(padding)
+
         output = _F.pad_bounding_box(self, padding, format=self.format)
 
         # Update output image size:
@@ -153,7 +160,7 @@ def rotate(
         angle: float,
         interpolation: InterpolationMode = InterpolationMode.NEAREST,
         expand: bool = False,
-        fill: Optional[List[float]] = None,
+        fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
         center: Optional[List[float]] = None,
     ) -> BoundingBox:
         from torchvision.prototype.transforms import functional as _F
@@ -173,7 +180,7 @@ def affine(
         scale: float,
         shear: List[float],
         interpolation: InterpolationMode = InterpolationMode.NEAREST,
-        fill: Optional[List[float]] = None,
+        fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
         center: Optional[List[float]] = None,
     ) -> BoundingBox:
         from torchvision.prototype.transforms import functional as _F
@@ -194,18 +201,9 @@ def perspective(
         self,
         perspective_coeffs: List[float],
         interpolation: InterpolationMode = InterpolationMode.BILINEAR,
-        fill: Optional[List[float]] = None,
+        fill: Optional[Union[int, float, Sequence[int], Sequence[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

@@ -120,7 +120,10 @@ def resized_crop(
         return self
 
     def pad(
-        self, padding: List[int], fill: Union[int, float, Sequence[float]] = 0, padding_mode: str = "constant"
+        self,
+        padding: Union[int, Sequence[int]],
+        fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
+        padding_mode: str = "constant",
     ) -> Any:
         return self
 
@@ -129,7 +132,7 @@ def rotate(
         angle: float,
         interpolation: InterpolationMode = InterpolationMode.NEAREST,
         expand: bool = False,
-        fill: Optional[List[float]] = None,
+        fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
         center: Optional[List[float]] = None,
     ) -> Any:
         return self
@@ -141,7 +144,7 @@ def affine(
         scale: float,
         shear: List[float],
         interpolation: InterpolationMode = InterpolationMode.NEAREST,
-        fill: Optional[List[float]] = None,
+        fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
         center: Optional[List[float]] = None,
     ) -> Any:
         return self
@@ -150,7 +153,7 @@ def perspective(
         self,
         perspective_coeffs: List[float],
         interpolation: InterpolationMode = InterpolationMode.BILINEAR,
-        fill: Optional[List[float]] = None,
+        fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
     ) -> Any:
         return self
 
@@ -186,12 +189,3 @@ def equalize(self) -> Any:
 
     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

torchvision/prototype/features/_image.py

@@ -164,10 +164,20 @@ def resized_crop(
         return Image.new_like(self, output)
 
     def pad(
-        self, padding: List[int], fill: Union[int, float, Sequence[float]] = 0, padding_mode: str = "constant"
+        self,
+        padding: Union[int, Sequence[int]],
+        fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
+        padding_mode: str = "constant",
     ) -> Image:
         from torchvision.prototype.transforms import functional as _F
 
+        # This cast does Sequence[int] -> List[int] and is required to make mypy happy
+        if not isinstance(padding, int):
+            padding = list(padding)
+
+        if fill is None:
+            fill = 0
+
         # PyTorch's pad supports only scalars on fill. So we need to overwrite the colour
         if isinstance(fill, (int, float)):
             output = _F.pad_image_tensor(self, padding, fill=fill, padding_mode=padding_mode)
@@ -183,10 +193,12 @@ def rotate(
         angle: float,
         interpolation: InterpolationMode = InterpolationMode.NEAREST,
         expand: bool = False,
-        fill: Optional[List[float]] = None,
+        fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
         center: Optional[List[float]] = None,
     ) -> Image:
-        from torchvision.prototype.transforms import functional as _F
+        from torchvision.prototype.transforms.functional import _geometry as _F
+
+        fill = _F._convert_fill_arg(fill)
 
         output = _F.rotate_image_tensor(
             self, angle, interpolation=interpolation, expand=expand, fill=fill, center=center
@@ -200,10 +212,12 @@ def affine(
         scale: float,
         shear: List[float],
         interpolation: InterpolationMode = InterpolationMode.NEAREST,
-        fill: Optional[List[float]] = None,
+        fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
         center: Optional[List[float]] = None,
     ) -> Image:
-        from torchvision.prototype.transforms import functional as _F
+        from torchvision.prototype.transforms.functional import _geometry as _F
+
+        fill = _F._convert_fill_arg(fill)
 
         output = _F.affine_image_tensor(
             self,
@@ -221,9 +235,11 @@ def perspective(
         self,
         perspective_coeffs: List[float],
         interpolation: InterpolationMode = InterpolationMode.BILINEAR,
-        fill: Optional[List[float]] = None,
+        fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
     ) -> Image:
-        from torchvision.prototype.transforms import functional as _F
+        from torchvision.prototype.transforms.functional import _geometry as _F
+
+        fill = _F._convert_fill_arg(fill)
 
         output = _F.perspective_image_tensor(self, perspective_coeffs, interpolation=interpolation, fill=fill)
         return Image.new_like(self, output)
@@ -293,25 +309,3 @@ def invert(self) -> Image:
 
         output = _F.invert_image_tensor(self)
         return Image.new_like(self, output)
-
-    def erase(self, i: int, j: int, h: int, w: int, v: torch.Tensor) -> Image:
-        from torchvision.prototype.transforms import functional as _F
-
-        output = _F.erase_image_tensor(self, i, j, h, w, v)
-        return Image.new_like(self, output)
-
-    def mixup(self, lam: float) -> Image:
-        if self.ndim < 4:
-            raise ValueError("Need a batch of images")
-        output = self.clone()
-        output = output.roll(1, -4).mul_(1 - lam).add_(output.mul_(lam))
-        return Image.new_like(self, output)
-
-    def cutmix(self, box: Tuple[int, int, int, int], lam_adjusted: float) -> Image:
-        if self.ndim < 4:
-            raise ValueError("Need a batch of images")
-        x1, y1, x2, y2 = box
-        image_rolled = self.roll(1, -4)
-        output = self.clone()
-        output[..., y1:y2, x1:x2] = image_rolled[..., y1:y2, x1:x2]
-        return Image.new_like(self, output)

torchvision/prototype/features/_label.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import Any, Optional, Sequence, cast, Union, Tuple
+from typing import Any, Optional, Sequence, cast, Union
 
 import torch
 from torchvision.prototype.utils._internal import apply_recursively
@@ -77,14 +77,3 @@ def new_like(
         return super().new_like(
             other, data, categories=categories if categories is not None else other.categories, **kwargs
         )
-
-    def mixup(self, lam: float) -> OneHotLabel:
-        if self.ndim < 2:
-            raise ValueError("Need a batch of one hot labels")
-        output = self.clone()
-        output = output.roll(1, -2).mul_(1 - lam).add_(output.mul_(lam))
-        return OneHotLabel.new_like(self, output)
-
-    def cutmix(self, box: Tuple[int, int, int, int], lam_adjusted: float) -> OneHotLabel:
-        box  # unused
-        return self.mixup(lam_adjusted)

torchvision/prototype/features/_segmentation_mask.py

@@ -1,8 +1,7 @@
 from __future__ import annotations
 
-from typing import Tuple, List, Optional, Union, Sequence
+from typing import List, Optional, Union, Sequence
 
-import torch
 from torchvision.transforms import InterpolationMode
 
 from ._feature import _Feature
@@ -61,10 +60,17 @@ def resized_crop(
         return SegmentationMask.new_like(self, output)
 
     def pad(
-        self, padding: List[int], fill: Union[int, float, Sequence[float]] = 0, padding_mode: str = "constant"
+        self,
+        padding: Union[int, Sequence[int]],
+        fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
+        padding_mode: str = "constant",
     ) -> SegmentationMask:
         from torchvision.prototype.transforms import functional as _F
 
+        # This cast does Sequence[int] -> List[int] and is required to make mypy happy
+        if not isinstance(padding, int):
+            padding = list(padding)
+
         output = _F.pad_segmentation_mask(self, padding, padding_mode=padding_mode)
         return SegmentationMask.new_like(self, output)
 
@@ -73,7 +79,7 @@ def rotate(
         angle: float,
         interpolation: InterpolationMode = InterpolationMode.NEAREST,
         expand: bool = False,
-        fill: Optional[List[float]] = None,
+        fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
         center: Optional[List[float]] = None,
     ) -> SegmentationMask:
         from torchvision.prototype.transforms import functional as _F
@@ -88,7 +94,7 @@ def affine(
         scale: float,
         shear: List[float],
         interpolation: InterpolationMode = InterpolationMode.NEAREST,
-        fill: Optional[List[float]] = None,
+        fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
         center: Optional[List[float]] = None,
     ) -> SegmentationMask:
         from torchvision.prototype.transforms import functional as _F
@@ -107,18 +113,9 @@ def perspective(
         self,
         perspective_coeffs: List[float],
         interpolation: InterpolationMode = InterpolationMode.NEAREST,
-        fill: Optional[List[float]] = None,
+        fill: Optional[Union[int, float, Sequence[int], Sequence[float]]] = None,
     ) -> SegmentationMask:
         from torchvision.prototype.transforms import functional as _F
 
         output = _F.perspective_segmentation_mask(self, perspective_coeffs)
         return SegmentationMask.new_like(self, output)
-
-    def erase(self, i: int, j: int, h: int, w: int, v: torch.Tensor) -> SegmentationMask:
-        raise TypeError("Erase transformation does not support segmentation masks")
-
-    def mixup(self, lam: float) -> SegmentationMask:
-        raise TypeError("Mixup transformation does not support segmentation masks")
-
-    def cutmix(self, box: Tuple[int, int, int, int], lam_adjusted: float) -> SegmentationMask:
-        raise TypeError("Cutmix transformation does not support segmentation masks")

torchvision/prototype/transforms/__init__.py

@@ -17,6 +17,8 @@
     RandomVerticalFlip,
     Pad,
     RandomZoomOut,
+    RandomRotation,
+    RandomAffine,
 )
 from ._meta import ConvertBoundingBoxFormat, ConvertImageDtype, ConvertImageColorSpace
 from ._misc import Identity, Normalize, ToDtype, Lambda