def _transform(self, input: Any, params: Dict[str, Any]) -> Any:
if isinstance(input,
(features.BoundingBox, features.SegmentationMask)):
raise TypeError(
f"{type(input).__name__}'s are not supported by {type(self).__name__}()"
)
elif isinstance(input, features.Image):
output = F.resized_crop_image_tensor(
input,
**params,
size=list(self.size),
interpolation=self.interpolation)
return features.Image.new_like(input, output)
elif isinstance(input, torch.Tensor):
return F.resized_crop_image_tensor(
input,
**params,
size=list(self.size),
interpolation=self.interpolation)
elif isinstance(input, PIL.Image.Image):
return F.resized_crop_image_pil(input,
**params,
size=list(self.size),
interpolation=self.interpolation)
else:
return input
def _transform(self, input: Any, params: Dict[str, Any]) -> Any:
if isinstance(input, features.Image):
output = F.resized_crop_image_tensor(
input, **params, size=list(self.size), interpolation=self.interpolation
)
return features.Image.new_like(input, output)
elif is_simple_tensor(input):
return F.resized_crop_image_tensor(input, **params, size=list(self.size), interpolation=self.interpolation)
elif isinstance(input, PIL.Image.Image):
return F.resized_crop_image_pil(input, **params, size=list(self.size), interpolation=self.interpolation)
else:
return input
def resized_crop(
self,
top: int,
left: int,
height: int,
width: int,
size: List[int],
interpolation: InterpolationMode = InterpolationMode.BILINEAR,
antialias: bool = False,
) -> Image:
from torchvision.prototype.transforms import functional as _F
output = _F.resized_crop_image_tensor(self,
top,
left,
height,
width,
size=list(size),
interpolation=interpolation,
antialias=antialias)
return Image.new_like(self, output)