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.BoundingBox):
output = F.horizontal_flip_bounding_box(input, format=input.format, image_size=input.image_size)
return features.BoundingBox.new_like(input, output)
elif isinstance(input, PIL.Image.Image):
return F.horizontal_flip_image_pil(input)
elif is_simple_tensor(input):
return F.horizontal_flip_image_tensor(input)
else:
return input
def horizontal_flip(self) -> Image:
from torchvision.prototype.transforms import functional as _F
output = _F.horizontal_flip_image_tensor(self)
return Image.new_like(self, output)