def __call__(
self, data: Mapping[Hashable, Union[np.ndarray, torch.Tensor]]
) -> Dict[Hashable, Union[np.ndarray, torch.Tensor]]:
d = dict(data)
sp_size = fall_back_tuple(self.rand_2d_elastic.spatial_size,
data[self.keys[0]].shape[1:])
self.randomize(spatial_size=sp_size)
if self.rand_2d_elastic.do_transform:
grid = self.rand_2d_elastic.deform_grid(spatial_size=sp_size)
grid = self.rand_2d_elastic.rand_affine_grid(grid=grid)
grid = _torch_interp(
input=grid.unsqueeze(0),
scale_factor=ensure_tuple_rep(
self.rand_2d_elastic.deform_grid.spacing, 2),
mode=InterpolateMode.BICUBIC.value,
align_corners=False,
)
grid = CenterSpatialCrop(roi_size=sp_size)(grid[0])
else:
grid = create_grid(spatial_size=sp_size)
for idx, key in enumerate(self.keys):
d[key] = self.rand_2d_elastic.resampler(
d[key],
grid,
mode=self.mode[idx],
padding_mode=self.padding_mode[idx])
return d
def __call__(self, data):
d = dict(data)
sp_size = fall_back_tuple(self.rand_2d_elastic.spatial_size,
data[self.keys[0]].shape[1:])
self.randomize(spatial_size=sp_size)
if self.rand_2d_elastic.do_transform:
grid = self.rand_2d_elastic.deform_grid(spatial_size=sp_size)
grid = self.rand_2d_elastic.rand_affine_grid(grid=grid)
grid = _torch_interp(
input=grid[None],
scale_factor=list(self.rand_2d_elastic.deform_grid.spacing),
mode=InterpolateMode.BICUBIC.value,
align_corners=False,
)
grid = CenterSpatialCrop(roi_size=sp_size)(grid[0])
else:
grid = create_grid(spatial_size=sp_size)
for idx, key in enumerate(self.keys):
d[key] = self.rand_2d_elastic.resampler(
d[key],
grid,
mode=self.mode[idx],
padding_mode=self.padding_mode[idx])
return d
def __call__(self, data):
d = dict(data)
spatial_size = self.rand_2d_elastic.spatial_size
self.randomize(spatial_size)
if self.rand_2d_elastic.do_transform:
grid = self.rand_2d_elastic.deform_grid(spatial_size)
grid = self.rand_2d_elastic.rand_affine_grid(grid=grid)
grid = _torch_interp(input=grid[None],
size=spatial_size,
mode="bicubic",
align_corners=False)[0]
else:
grid = create_grid(spatial_size)
for idx, key in enumerate(self.keys):
d[key] = self.rand_2d_elastic.resampler(
d[key],
grid,
padding_mode=self.padding_mode[idx],
mode=self.mode[idx])
return d