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enhance random range parameters #1635

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Merged
rijobro merged 6 commits into from
Feb 25, 2021
Merged

enhance random range parameters #1635

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69bd730
enhance random range parameters
rijobro Feb 24, 2021
db4f6ca
add missing import
rijobro Feb 24, 2021
fd035a4
Merge branch 'master' into enhance_random_range_params
rijobro Feb 24, 2021
187c1c2
Merge branch 'master' into enhance_random_range_params
rijobro Feb 25, 2021
0d8bdae
bug fixes
rijobro Feb 25, 2021
26a3761
Merge remote-tracking branch 'MONAI/master' into enhance_random_range…
rijobro Feb 25, 2021
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158 changes: 77 additions & 81 deletions monai/transforms/spatial/array.py
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Original file line number Diff line number Diff line change
Expand Up @@ -42,6 +42,7 @@
ensure_tuple_rep,
ensure_tuple_size,
fall_back_tuple,
issequenceiterable,
optional_import,
)

Expand Down Expand Up @@ -69,6 +70,8 @@
"Rand3DElastic",
]

RandRange = Optional[Union[Sequence[Union[Tuple[float, float], float]], float]]


class Spacing(Transform):
"""
Expand Down Expand Up @@ -965,30 +968,25 @@ class RandAffineGrid(Randomizable, Transform):

def __init__(
self,
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,
rotate_range: RandRange = None,
shear_range: RandRange = None,
translate_range: RandRange = None,
scale_range: RandRange = None,
as_tensor_output: bool = True,
device: Optional[torch.device] = None,
) -> None:
"""
Args:
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])`. Similarly, `rotate_range[1]` and
`rotate_range[2]` are used in 3D affine for the range of 2nd and 3rd axes.
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]` to
`shear_range[N]` controls the range of the uniform distribution used to generate the 2nd to
N-th 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]`
to `translate_range[N]` controls the range of the uniform distribution used to generate
the 2nd to N-th 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]` to
`scale_range[N]` controls the range of the uniform distribution used to generate the 2nd to
N-th parameter.
rotate_range: angle range in radians. If element `i` is iterable, then
`uniform[-rotate_range[i][0], rotate_range[i][1])` will be used to generate the rotation parameter
for the ith dimension. If not, `uniform[-rotate_range[i], rotate_range[i])` will be used. This can
be altered on a per-dimension basis. E.g., `((0,3), 1, ...)`: for dim0, rotation will be in range
`[0, 3]`, and for dim1 `[-1, 1]` will be used. Setting a single value will use `[-x, x]` for dim0
and nothing for the remaining dimensions.
shear_range: shear_range with format matching `rotate_range`.
translate_range: translate_range with format matching `rotate_range`.
scale_range: scaling_range with format matching `rotate_range`. A value of 1.0 is added to the result.
This allows 0 to correspond to no change (i.e., a scaling of 1).
as_tensor_output: whether to output tensor instead of numpy array.
defaults to True.
device: device to store the output grid data.
Expand All @@ -1012,15 +1010,22 @@ def __init__(
self.as_tensor_output = as_tensor_output
self.device = device

def _get_rand_param(self, param_range, add_scalar: float = 0.0):
out_param = []
for f in param_range:
if issequenceiterable(f):
if len(f) != 2:
raise ValueError("If giving range as [min,max], should only have two elements per dim.")
out_param.append(self.R.uniform(f[0], f[1]) + add_scalar)
elif f is not None:
out_param.append(self.R.uniform(-f, f) + add_scalar)
return out_param

def randomize(self, data: Optional[Any] = None) -> None:
if self.rotate_range:
self.rotate_params = [self.R.uniform(-f, f) for f in self.rotate_range if f is not None]
if self.shear_range:
self.shear_params = [self.R.uniform(-f, f) for f in self.shear_range if f is not None]
if self.translate_range:
self.translate_params = [self.R.uniform(-f, f) for f in self.translate_range if f is not None]
if self.scale_range:
self.scale_params = [self.R.uniform(-f, f) + 1.0 for f in self.scale_range if f is not None]
self.rotate_params = self._get_rand_param(self.rotate_range)
self.shear_params = self._get_rand_param(self.shear_range)
self.translate_params = self._get_rand_param(self.translate_range)
self.scale_params = self._get_rand_param(self.scale_range, 1.0)

def __call__(
self, spatial_size: Optional[Sequence[int]] = None, grid: Optional[Union[np.ndarray, torch.Tensor]] = None
Expand Down Expand Up @@ -1282,11 +1287,11 @@ class RandAffine(Randomizable, Transform):
def __init__(
self,
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,
spatial_size: Optional[Union[Sequence[float], float]] = None,
rotate_range: RandRange = None,
shear_range: RandRange = None,
translate_range: RandRange = None,
scale_range: RandRange = None,
spatial_size: Optional[Union[Sequence[int], int]] = None,
mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.REFLECTION,
as_tensor_output: bool = True,
Expand All @@ -1296,21 +1301,16 @@ def __init__(
Args:
prob: probability of returning a randomized affine grid.
defaults to 0.1, with 10% chance returns a randomized grid.
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])`. Similarly, `rotate_range[1]` and
`rotate_range[2]` are used in 3D affine for the range of 2nd and 3rd axes.
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]` to
`shear_range[N]` controls the range of the uniform distribution used to generate the 2nd to
N-th 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]`
to `translate_range[N]` controls the range of the uniform distribution used to generate
the 2nd to N-th 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]` to
`scale_range[N]` controls the range of the uniform distribution used to generate the 2nd to
N-th parameter.
rotate_range: angle range in radians. If element `i` is iterable, then
`uniform[-rotate_range[i][0], rotate_range[i][1])` will be used to generate the rotation parameter
for the ith dimension. If not, `uniform[-rotate_range[i], rotate_range[i])` will be used. This can
be altered on a per-dimension basis. E.g., `((0,3), 1, ...)`: for dim0, rotation will be in range
`[0, 3]`, and for dim1 `[-1, 1]` will be used. Setting a single value will use `[-x, x]` for dim0
and nothing for the remaining dimensions.
shear_range: shear_range with format matching `rotate_range`.
translate_range: translate_range with format matching `rotate_range`.
scale_range: scaling_range with format matching `rotate_range`. A value of 1.0 is added to the result.
This allows 0 to correspond to no change (i.e., a scaling of 1).
spatial_size: output image spatial size.
if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,
the transform will use the spatial size of `img`.
Expand Down Expand Up @@ -1404,11 +1404,11 @@ def __init__(
spacing: Union[Tuple[float, float], float],
magnitude_range: Tuple[float, float],
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,
spatial_size: Optional[Union[Sequence[int], int]] = None,
rotate_range: RandRange = None,
shear_range: RandRange = None,
translate_range: RandRange = None,
scale_range: RandRange = None,
spatial_size: Optional[Union[Tuple[int, int], int]] = None,
mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.REFLECTION,
as_tensor_output: bool = False,
Expand All @@ -1421,17 +1421,16 @@ def __init__(
prob: probability of returning a randomized elastic transform.
defaults to 0.1, with 10% chance returns a randomized elastic transform,
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.
rotate_range: angle range in radians. If element `i` is iterable, then
`uniform[-rotate_range[i][0], rotate_range[i][1])` will be used to generate the rotation parameter
for the ith dimension. If not, `uniform[-rotate_range[i], rotate_range[i])` will be used. This can
be altered on a per-dimension basis. E.g., `((0,3), 1, ...)`: for dim0, rotation will be in range
`[0, 3]`, and for dim1 `[-1, 1]` will be used. Setting a single value will use `[-x, x]` for dim0
and nothing for the remaining dimensions.
shear_range: shear_range with format matching `rotate_range`.
translate_range: translate_range with format matching `rotate_range`.
scale_range: scaling_range with format matching `rotate_range`. A value of 1.0 is added to the result.
This allows 0 to correspond to no change (i.e., a scaling of 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`.
Expand Down Expand Up @@ -1532,11 +1531,11 @@ def __init__(
sigma_range: Tuple[float, float],
magnitude_range: Tuple[float, float],
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,
spatial_size: Optional[Union[Sequence[int], int]] = None,
rotate_range: RandRange = None,
shear_range: RandRange = None,
translate_range: RandRange = None,
scale_range: RandRange = None,
spatial_size: Optional[Union[Tuple[int, int, int], int]] = None,
mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.REFLECTION,
as_tensor_output: bool = False,
Expand All @@ -1551,19 +1550,16 @@ def __init__(
prob: probability of returning a randomized elastic transform.
defaults to 0.1, with 10% chance returns a randomized elastic transform,
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])`. Similarly, `rotate_range[1]` and
`rotate_range[2]` are used in 3D affine for the range of 2nd and 3rd axes.
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]` and `shear_range[2]`
controls the range of the uniform distribution used to generate the 2nd and 3rd parameters.
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]` and
`translate_range[2]` controls the range of the uniform distribution used to generate
the 2nd and 3rd parameters.
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]` and `scale_range[2]`
controls the range of the uniform distribution used to generate the 2nd and 3rd parameters.
rotate_range: angle range in radians. If element `i` is iterable, then
`uniform[-rotate_range[i][0], rotate_range[i][1])` will be used to generate the rotation parameter
for the ith dimension. If not, `uniform[-rotate_range[i], rotate_range[i])` will be used. This can
be altered on a per-dimension basis. E.g., `((0,3), 1, ...)`: for dim0, rotation will be in range
`[0, 3]`, and for dim1 `[-1, 1]` will be used. Setting a single value will use `[-x, x]` for dim0
and nothing for the remaining dimensions.
shear_range: shear_range with format matching `rotate_range`.
translate_range: translate_range with format matching `rotate_range`.
scale_range: scaling_range with format matching `rotate_range`. A value of 1.0 is added to the result.
This allows 0 to correspond to no change (i.e., a scaling of 1).
spatial_size: specifying output image spatial size [h, w, d].
if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,
the transform will use the spatial size of `img`.
Expand Down
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