def resize_image_pil(
img: PIL.Image.Image,
size: Union[Sequence[int], int],
interpolation: InterpolationMode = InterpolationMode.BILINEAR,
max_size: Optional[int] = None,
) -> PIL.Image.Image:
return _FP.resize(img, size, interpolation=pil_modes_mapping[interpolation], max_size=max_size)
def test_resize(self):
script_fn = torch.jit.script(F_t.resize)
tensor, pil_img = self._create_data(26, 36, device=self.device)
for dt in [None, torch.float32, torch.float64, torch.float16]:
if dt == torch.float16 and torch.device(self.device).type == "cpu":
# skip float16 on CPU case
continue
if dt is not None:
# This is a trivial cast to float of uint8 data to test all cases
tensor = tensor.to(dt)
for size in [32, 26, [
32,
], [32, 32], (32, 32), [26, 35]]:
for interpolation in [BILINEAR, BICUBIC, NEAREST]:
resized_tensor = F_t.resize(tensor,
size=size,
interpolation=interpolation)
resized_pil_img = F_pil.resize(pil_img,
size=size,
interpolation=interpolation)
self.assertEqual(resized_tensor.size()[1:],
resized_pil_img.size[::-1],
msg="{}, {}".format(size, interpolation))
if interpolation != NEAREST:
# We can not check values if mode = NEAREST, as results are different
# E.g. resized_tensor = [[a, a, b, c, d, d, e, ...]]
# E.g. resized_pil_img = [[a, b, c, c, d, e, f, ...]]
resized_tensor_f = resized_tensor
# we need to cast to uint8 to compare with PIL image
if resized_tensor_f.dtype == torch.uint8:
resized_tensor_f = resized_tensor_f.to(torch.float)
# Pay attention to high tolerance for MAE
self.approxEqualTensorToPIL(resized_tensor_f,
resized_pil_img,
tol=8.0,
msg="{}, {}".format(
size, interpolation))
if isinstance(size, int):
script_size = [
size,
]
else:
script_size = size
resize_result = script_fn(tensor,
size=script_size,
interpolation=interpolation)
self.assertTrue(resized_tensor.equal(resize_result),
msg="{}, {}".format(size, interpolation))
def resize_image_pil(
img: PIL.Image.Image,
size: Union[Sequence[int], int],
interpolation: InterpolationMode = InterpolationMode.BILINEAR,
max_size: Optional[int] = None,
) -> PIL.Image.Image:
if isinstance(size, int):
size = [size, size]
# Explicitly cast size to list otherwise mypy issue: incompatible type "Sequence[int]"; expected "List[int]"
size: List[int] = list(size)
size = _compute_output_size(img.size[::-1], size=size, max_size=max_size)
return _FP.resize(img,
size,
interpolation=pil_modes_mapping[interpolation])
def forward(self, img: Image):
img = tF_pil.resize(img=img,
size=self.scaled_size,
interpolation=PIL.Image.BICUBIC)
return tF.center_crop(img, self.size)
def forward(self, img: Image):
scaled_size = tuple(x + 32 for x in img.size)
img = tF_pil.resize(img=img,
size=scaled_size,
interpolation=PIL.Image.BICUBIC)
return tF.center_crop(img, self.size)
