def __init__(
self,
keys: KeysCollection,
spatial_size,
spacing,
magnitude_range,
prob: float = 0.1,
rotate_range=None,
shear_range=None,
translate_range=None,
scale_range=None,
mode: GridSampleModeSequence = GridSampleMode.BILINEAR,
padding_mode: GridSamplePadModeSequence = GridSamplePadMode.REFLECTION,
as_tensor_output: bool = False,
device: Optional[torch.device] = None,
):
"""
Args:
keys: keys of the corresponding items to be transformed.
spatial_size (2 ints): specifying output image spatial size [h, w].
if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,
the transform will use the spatial size of `img`.
spacing (2 ints): distance in between the control points.
magnitude_range (2 ints): the random offsets will be generated from
``uniform[magnitude[0], magnitude[1])``.
prob: probability of returning a randomized affine grid.
defaults to 0.1, with 10% chance returns a randomized grid,
otherwise returns a ``spatial_size`` centered area extracted from the input image.
mode: {``"bilinear"``, ``"nearest"``}
Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
It also can be a sequence of string, each element corresponds to a key in ``keys``.
padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
Padding mode for outside grid values. Defaults to ``"reflection"``.
See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
It also can be a sequence of string, each element corresponds to a key in ``keys``.
as_tensor_output: the computation is implemented using pytorch tensors, this option specifies
whether to convert it back to numpy arrays.
device (torch.device): device on which the tensor will be allocated.
See also:
- :py:class:`RandAffineGrid` for the random affine parameters configurations.
- :py:class:`Affine` for the affine transformation parameters configurations.
"""
super().__init__(keys)
self.rand_2d_elastic = Rand2DElastic(
spacing=spacing,
magnitude_range=magnitude_range,
prob=prob,
rotate_range=rotate_range,
shear_range=shear_range,
translate_range=translate_range,
scale_range=scale_range,
spatial_size=spatial_size,
as_tensor_output=as_tensor_output,
device=device,
)
self.mode = ensure_tuple_rep(mode, len(self.keys))
self.padding_mode = ensure_tuple_rep(padding_mode, len(self.keys))
def __init__(
self,
keys,
spatial_size,
spacing,
magnitude_range,
prob=0.1,
rotate_range=None,
shear_range=None,
translate_range=None,
scale_range=None,
mode="bilinear",
padding_mode="zeros",
as_tensor_output=False,
device=None,
):
"""
Args:
keys (Hashable items): keys of the corresponding items to be transformed.
spatial_size (2 ints): specifying output image spatial size [h, w].
spacing (2 ints): distance in between the control points.
magnitude_range (2 ints): the random offsets will be generated from
``uniform[magnitude[0], magnitude[1])``.
prob (float): probability of returning a randomized affine grid.
defaults to 0.1, with 10% chance returns a randomized grid,
otherwise returns a ``spatial_size`` centered area extracted from the input image.
mode ('nearest'|'bilinear'): interpolation order. Defaults to ``'bilinear'``.
if mode is a tuple of interpolation mode strings, each string corresponds to a key in ``keys``.
this is useful to set different modes for different data items.
padding_mode ('zeros'|'border'|'reflection'): mode of handling out of range indices.
Defaults to ``'zeros'``.
as_tensor_output (bool): the computation is implemented using pytorch tensors, this option specifies
whether to convert it back to numpy arrays.
device (torch.device): device on which the tensor will be allocated.
See also:
- :py:class:`RandAffineGrid` for the random affine parameters configurations.
- :py:class:`Affine` for the affine transformation parameters configurations.
"""
super().__init__(keys)
default_mode = "bilinear" if isinstance(mode, (tuple, list)) else mode
self.rand_2d_elastic = Rand2DElastic(
spacing=spacing,
magnitude_range=magnitude_range,
prob=prob,
rotate_range=rotate_range,
shear_range=shear_range,
translate_range=translate_range,
scale_range=scale_range,
spatial_size=spatial_size,
mode=default_mode,
padding_mode=padding_mode,
as_tensor_output=as_tensor_output,
device=device,
)
self.mode = mode
def __init__(
self,
keys: KeysCollection,
spacing: Union[Tuple[float, float], float],
magnitude_range: Tuple[float, float],
spatial_size: Optional[Union[Sequence[int], int]] = None,
prob: float = 0.1,
rotate_range: Optional[Union[Sequence[float], float]] = None,
shear_range: Optional[Union[Sequence[float], float]] = None,
translate_range: Optional[Union[Sequence[float], float]] = None,
scale_range: Optional[Union[Sequence[float], float]] = None,
mode: GridSampleModeSequence = GridSampleMode.BILINEAR,
padding_mode: GridSamplePadModeSequence = GridSamplePadMode.REFLECTION,
as_tensor_output: bool = False,
device: Optional[torch.device] = None,
) -> None:
"""
Args:
keys: keys of the corresponding items to be transformed.
spacing: distance in between the control points.
magnitude_range: 2 int numbers, the random offsets will be generated from
``uniform[magnitude[0], magnitude[1])``.
spatial_size: specifying output image spatial size [h, w].
if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,
the transform will use the spatial size of `img`.
if the components of the `spatial_size` are non-positive values, the transform will use the
corresponding components of img size. For example, `spatial_size=(32, -1)` will be adapted
to `(32, 64)` if the second spatial dimension size of img is `64`.
prob: probability of returning a randomized affine grid.
defaults to 0.1, with 10% chance returns a randomized grid,
otherwise returns a ``spatial_size`` centered area extracted from the input image.
rotate_range: angle range in radians. rotate_range[0] with be used to generate the 1st rotation
parameter from `uniform[-rotate_range[0], rotate_range[0])`.
shear_range: shear_range[0] with be used to generate the 1st shearing parameter from
`uniform[-shear_range[0], shear_range[0])`. Similarly, `shear_range[1]` controls
the range of the uniform distribution used to generate the 2nd parameter.
translate_range : translate_range[0] with be used to generate the 1st shift parameter from
`uniform[-translate_range[0], translate_range[0])`. Similarly, `translate_range[1]` controls
the range of the uniform distribution used to generate the 2nd parameter.
scale_range: scaling_range[0] with be used to generate the 1st scaling factor from
`uniform[-scale_range[0], scale_range[0]) + 1.0`. Similarly, `scale_range[1]` controls
the range of the uniform distribution used to generate the 2nd parameter.
mode: {``"bilinear"``, ``"nearest"``}
Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
It also can be a sequence of string, each element corresponds to a key in ``keys``.
padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
Padding mode for outside grid values. Defaults to ``"reflection"``.
See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
It also can be a sequence of string, each element corresponds to a key in ``keys``.
as_tensor_output: the computation is implemented using pytorch tensors, this option specifies
whether to convert it back to numpy arrays.
device: device on which the tensor will be allocated.
See also:
- :py:class:`RandAffineGrid` for the random affine parameters configurations.
- :py:class:`Affine` for the affine transformation parameters configurations.
"""
super().__init__(keys)
self.rand_2d_elastic = Rand2DElastic(
spacing=spacing,
magnitude_range=magnitude_range,
prob=prob,
rotate_range=rotate_range,
shear_range=shear_range,
translate_range=translate_range,
scale_range=scale_range,
spatial_size=spatial_size,
as_tensor_output=as_tensor_output,
device=device,
)
self.mode = ensure_tuple_rep(mode, len(self.keys))
self.padding_mode = ensure_tuple_rep(padding_mode, len(self.keys))
