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Remove apply_same_field #3556

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Merged
merged 2 commits into from
Dec 30, 2021
Merged

Remove apply_same_field #3556

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a8a7d21
Remove apply_same_field
ericspod Dec 30, 2021
c3208c2
Merge branch 'dev' into remove_same_field
wyli Dec 30, 2021
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18 changes: 0 additions & 18 deletions monai/transforms/smooth_field/dictionary.py
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Original file line number Diff line number Diff line change
Expand Up @@ -50,7 +50,6 @@ class RandSmoothFieldAdjustContrastd(RandomizableTransform, MapTransform):
align_corners: if True align the corners when upsampling field
prob: probability transform is applied
gamma: (min, max) range for exponential field
apply_same_field: if True, apply the same field to each key, otherwise randomize individually
device: Pytorch device to define field on
"""

Expand All @@ -66,13 +65,11 @@ def __init__(
align_corners: Optional[bool] = None,
prob: float = 0.1,
gamma: Union[Sequence[float], float] = (0.5, 4.5),
apply_same_field: bool = True,
device: Optional[torch.device] = None,
):
RandomizableTransform.__init__(self, prob)
MapTransform.__init__(self, keys)

self.apply_same_field = apply_same_field
self.mode = ensure_tuple_rep(mode, len(self.keys))

self.trans = RandSmoothFieldAdjustContrast(
Expand Down Expand Up @@ -108,9 +105,6 @@ def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Mapping[Hashable
d = dict(data)

for idx, key in enumerate(self.key_iterator(d)):
if not self.apply_same_field:
self.randomize() # new field for every key

self.trans.set_mode(self.mode[idx % len(self.mode)])
d[key] = self.trans(d[key], False)

Expand All @@ -134,7 +128,6 @@ class RandSmoothFieldAdjustIntensityd(RandomizableTransform, MapTransform):
align_corners: if True align the corners when upsampling field
prob: probability transform is applied
gamma: (min, max) range of intensity multipliers
apply_same_field: if True, apply the same field to each key, otherwise randomize individually
device: Pytorch device to define field on
"""

Expand All @@ -150,13 +143,11 @@ def __init__(
align_corners: Optional[bool] = None,
prob: float = 0.1,
gamma: Union[Sequence[float], float] = (0.1, 1.0),
apply_same_field: bool = True,
device: Optional[torch.device] = None,
):
RandomizableTransform.__init__(self, prob)
MapTransform.__init__(self, keys)

self.apply_same_field = apply_same_field
self.mode = ensure_tuple_rep(mode, len(self.keys))

self.trans = RandSmoothFieldAdjustIntensity(
Expand Down Expand Up @@ -190,9 +181,6 @@ def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Mapping[Hashable
d = dict(data)

for idx, key in enumerate(self.key_iterator(d)):
if not self.apply_same_field:
self.randomize() # new field for every key

self.trans.set_mode(self.mode[idx % len(self.mode)])
d[key] = self.trans(d[key], False)

Expand Down Expand Up @@ -220,7 +208,6 @@ class RandSmoothDeformd(RandomizableTransform, MapTransform):
grid_mode: interpolation mode used for sampling input using deformation grid
grid_padding_mode: padding mode used for sampling input using deformation grid
grid_align_corners: if True align the corners when sampling the deformation grid
apply_same_field: if True, apply the same field to each key, otherwise randomize individually
device: Pytorch device to define field on
"""

Expand All @@ -240,15 +227,13 @@ def __init__(
grid_mode: Union[GridSampleModeType, Sequence[GridSampleModeType]] = GridSampleMode.NEAREST,
grid_padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,
grid_align_corners: Optional[bool] = False,
apply_same_field: bool = True,
device: Optional[torch.device] = None,
):
RandomizableTransform.__init__(self, prob)
MapTransform.__init__(self, keys)

self.field_mode = ensure_tuple_rep(field_mode, len(self.keys))
self.grid_mode = ensure_tuple_rep(grid_mode, len(self.keys))
self.apply_same_field = apply_same_field

self.trans = RandSmoothDeform(
rand_size=rand_size,
Expand Down Expand Up @@ -285,9 +270,6 @@ def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Mapping[Hashable
d = dict(data)

for idx, key in enumerate(self.key_iterator(d)):
if not self.apply_same_field:
self.randomize() # new field for every key

self.trans.set_field_mode(self.field_mode[idx % len(self.field_mode)])
self.trans.set_grid_mode(self.grid_mode[idx % len(self.grid_mode)])

Expand Down