port RandomHorizontalFlip to prototype API

test/test_prototype_transforms.py

@@ -2,9 +2,10 @@
 
 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 torchvision.prototype import transforms, features
-from torchvision.transforms.functional import to_pil_image
+from torchvision.transforms.functional import to_pil_image, pil_to_tensor
 
 
 def make_vanilla_tensor_images(*args, **kwargs):
@@ -66,10 +67,10 @@ def parametrize_from_transforms(*transforms):
 class TestSmoke:
     @parametrize_from_transforms(
         transforms.RandomErasing(p=1.0),
-        transforms.HorizontalFlip(),
         transforms.Resize([16, 16]),
         transforms.CenterCrop([16, 16]),
         transforms.ConvertImageDtype(),
+        transforms.RandomHorizontalFlip(),
     )
     def test_common(self, transform, input):
         transform(input)
@@ -188,3 +189,56 @@ def test_random_resized_crop(self, transform, input):
     )
     def test_convert_image_color_space(self, transform, input):
         transform(input)
+
+
+@pytest.mark.parametrize("p", [0.0, 1.0])
+class TestRandomHorizontalFlip:
+    def input_expected_image_tensor(self, p, dtype=torch.float32):
+        input = torch.tensor([[[0, 1], [0, 1]], [[1, 0], [1, 0]]], dtype=dtype)
+        expected = torch.tensor([[[1, 0], [1, 0]], [[0, 1], [0, 1]]], dtype=dtype)
+
+        return input, expected if p == 1 else input
+
+    def test_simple_tensor(self, p):
+        input, expected = self.input_expected_image_tensor(p)
+        transform = transforms.RandomHorizontalFlip(p=p)
+
+        actual = transform(input)
+
+        assert_equal(expected, actual)
+
+    def test_pil_image(self, p):
+        input, expected = self.input_expected_image_tensor(p, dtype=torch.uint8)
+        transform = transforms.RandomHorizontalFlip(p=p)
+
+        actual = transform(to_pil_image(input))
+
+        assert_equal(expected, pil_to_tensor(actual))
+
+    def test_features_image(self, p):
+        input, expected = self.input_expected_image_tensor(p)
+        transform = transforms.RandomHorizontalFlip(p=p)
+
+        actual = transform(features.Image(input))
+
+        assert_equal(features.Image(expected), actual)
+
+    def test_features_segmentation_mask(self, p):
+        input, expected = self.input_expected_image_tensor(p)
+        transform = transforms.RandomHorizontalFlip(p=p)
+
+        actual = transform(features.SegmentationMask(input))
+
+        assert_equal(features.SegmentationMask(expected), actual)
+
+    def test_features_bounding_box(self, p):
+        input = features.BoundingBox([0, 0, 5, 5], format=features.BoundingBoxFormat.XYXY, image_size=(10, 10))
+        transform = transforms.RandomHorizontalFlip(p=p)
+
+        actual = transform(input)
+
+        expected_image_tensor = torch.tensor([5, 0, 10, 5]) if p == 1.0 else input
+        expected = features.BoundingBox.new_like(input, data=expected_image_tensor)
+        assert_equal(expected, actual)
+        assert actual.format == expected.format
+        assert actual.image_size == expected.image_size

torchvision/prototype/transforms/__init__.py

@@ -8,13 +8,13 @@
 from ._auto_augment import RandAugment, TrivialAugmentWide, AutoAugment, AugMix
 from ._container import Compose, RandomApply, RandomChoice, RandomOrder
 from ._geometry import (
-    HorizontalFlip,
     Resize,
     CenterCrop,
     RandomResizedCrop,
     FiveCrop,
     TenCrop,
     BatchMultiCrop,
+    RandomHorizontalFlip,
     RandomZoomOut,
 )
 from ._meta import ConvertBoundingBoxFormat, ConvertImageDtype, ConvertImageColorSpace

torchvision/prototype/transforms/_geometry.py

@@ -13,11 +13,25 @@
 from ._utils import query_image, get_image_dimensions, has_any, is_simple_tensor
 
 
-class HorizontalFlip(Transform):
+class RandomHorizontalFlip(Transform):
+    def __init__(self, p: float = 0.5) -> None:
+        super().__init__()
+        self.p = p
+
+    def forward(self, *inputs: Any) -> Any:
+        sample = inputs if len(inputs) > 1 else inputs[0]
+        if torch.rand(1) >= self.p:
+            return sample
+
+        return super().forward(sample)
+
     def _transform(self, input: Any, params: Dict[str, Any]) -> Any:
         if isinstance(input, features.Image):
             output = F.horizontal_flip_image_tensor(input)
             return features.Image.new_like(input, output)
+        elif isinstance(input, features.SegmentationMask):
+            output = F.horizontal_flip_segmentation_mask(input)
+            return features.SegmentationMask.new_like(input, output)
         elif isinstance(input, features.BoundingBox):
             output = F.horizontal_flip_bounding_box(input, format=input.format, image_size=input.image_size)
             return features.BoundingBox.new_like(input, output)

torchvision/prototype/transforms/functional/__init__.py

@@ -40,6 +40,7 @@
     horizontal_flip_bounding_box,
     horizontal_flip_image_tensor,
     horizontal_flip_image_pil,
+    horizontal_flip_segmentation_mask,
     resize_bounding_box,
     resize_image_tensor,
     resize_image_pil,

torchvision/prototype/transforms/functional/_geometry.py

@@ -15,6 +15,10 @@
 horizontal_flip_image_pil = _FP.hflip
 
 
+def horizontal_flip_segmentation_mask(segmentation_mask: torch.Tensor) -> torch.Tensor:
+    return horizontal_flip_image_tensor(segmentation_mask)
+
+
 def horizontal_flip_bounding_box(
     bounding_box: torch.Tensor, format: features.BoundingBoxFormat, image_size: Tuple[int, int]
 ) -> torch.Tensor: