[prototype] Clean up and port the resize kernel in V2 (#6892)

* Ported `resize` * Align with previous behaviour * Update torchvision/prototype/transforms/functional/_geometry.py Co-authored-by: Philip Meier <github.pmeier@posteo.de> * Moving input verification on top of method. Co-authored-by: Philip Meier <github.pmeier@posteo.de>

Author: datumbox

torchvision/prototype/transforms/functional/_geometry.py

@@ -4,6 +4,7 @@
 
 import PIL.Image
 import torch
+from torch.nn.functional import interpolate
 from torchvision.prototype import features
 from torchvision.transforms import functional_pil as _FP, functional_tensor as _FT
 from torchvision.transforms.functional import (
@@ -115,20 +116,37 @@ def resize_image_tensor(
  max_size: Optional[int] = None,
  antialias: bool = False,
 ) -> torch.Tensor:
+ align_corners: Optional[bool] = None
+ if interpolation == InterpolationMode.BILINEAR or interpolation == InterpolationMode.BICUBIC:
+ align_corners = False
+ elif antialias:
+ raise ValueError("Antialias option is supported for bilinear and bicubic interpolation modes only")
+
  shape = image.shape
  num_channels, old_height, old_width = shape[-3:]
  new_height, new_width = _compute_resized_output_size((old_height, old_width), size=size, max_size=max_size)
 
  if image.numel() > 0:
  image = image.reshape(-1, num_channels, old_height, old_width)
 
- image = _FT.resize(
+ dtype = image.dtype
+ need_cast = dtype not in (torch.float32, torch.float64)
+ if need_cast:
+ image = image.to(dtype=torch.float32)
+
+ image = interpolate(
  image,
  size=[new_height, new_width],
- interpolation=interpolation.value,
+ mode=interpolation.value,
+ align_corners=align_corners,
  antialias=antialias,
  )
 
+ if need_cast:
+ if interpolation == InterpolationMode.BICUBIC and dtype == torch.uint8:
+ image = image.clamp_(min=0, max=255)
+ image = image.round_().to(dtype=dtype)
+
  return image.reshape(shape[:-3] + (num_channels, new_height, new_width))
 
 
@@ -1312,9 +1330,11 @@ def resized_crop(
 
 def _parse_five_crop_size(size: List[int]) -> List[int]:
  if isinstance(size, numbers.Number):
- size = [int(size), int(size)]
+ s = int(size)
+ size = [s, s]
  elif isinstance(size, (tuple, list)) and len(size) == 1:
- size = [size[0], size[0]]
+ s = size[0]
+ size = [s, s]
 
  if len(size) != 2:
  raise ValueError("Please provide only two dimensions (h, w) for size.")