def _transform(self, input: Any, params: Dict[str, Any]) -> Any:
if isinstance(input, features.Image):
output = F.resize_image_tensor(input,
self.size,
interpolation=self.interpolation)
return features.Image.new_like(input, output)
elif isinstance(input, features.SegmentationMask):
output = F.resize_segmentation_mask(input, self.size)
return features.SegmentationMask.new_like(input, output)
elif isinstance(input, features.BoundingBox):
output = F.resize_bounding_box(input,
self.size,
image_size=input.image_size)
return features.BoundingBox.new_like(input,
output,
image_size=cast(
Tuple[int, int],
tuple(self.size)))
elif isinstance(input, PIL.Image.Image):
return F.resize_image_pil(input,
self.size,
interpolation=self.interpolation)
elif is_simple_tensor(input):
return F.resize_image_tensor(input,
self.size,
interpolation=self.interpolation)
else:
return input
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)