def __init__(
self,
keys: KeysCollection,
sigmoid: Union[Sequence[bool], bool] = False,
softmax: Union[Sequence[bool], bool] = False,
other: Optional[Union[Sequence[Callable], Callable]] = None,
) -> None:
"""
Args:
keys: keys of the corresponding items to model output and label.
See also: :py:class:`monai.transforms.compose.MapTransform`
sigmoid: whether to execute sigmoid function on model output before transform.
it also can be a sequence of bool, each element corresponds to a key in ``keys``.
softmax: whether to execute softmax function on model output before transform.
it also can be a sequence of bool, each element corresponds to a key in ``keys``.
other: callable function to execute other activation layers,
for example: `other = lambda x: torch.tanh(x)`. it also can be a sequence of Callable, each
element corresponds to a key in ``keys``.
"""
super().__init__(keys)
self.sigmoid = ensure_tuple_rep(sigmoid, len(self.keys))
self.softmax = ensure_tuple_rep(softmax, len(self.keys))
self.other = ensure_tuple_rep(other, len(self.keys))
self.converter = Activations()
def __init__(self,
keys: Hashable,
output_postfix: str = "act",
sigmoid=False,
softmax=False,
other=None):
"""
Args:
keys (hashable items): keys of the corresponding items to model output and label.
See also: :py:class:`monai.transforms.compose.MapTransform`
output_postfix (str): the postfix string to construct keys to store converted data.
for example: if the keys of input data is `pred` and `label`, output_postfix is `act`,
the output data keys will be: `pred_act`, `label_act`.
if set to None, will replace the original data with the same key.
sigmoid (bool, tuple or list of bool): whether to execute sigmoid function on model
output before transform.
softmax (bool, tuple or list of bool): whether to execute softmax function on model
output before transform.
other (Callable, tuple or list of Callables): callable function to execute other activation layers,
for example: `other = lambda x: torch.tanh(x)`
"""
super().__init__(keys)
if output_postfix is not None and not isinstance(output_postfix, str):
raise ValueError("output_postfix must be a string.")
self.output_postfix = output_postfix
self.sigmoid = ensure_tuple_rep(sigmoid, len(self.keys))
self.softmax = ensure_tuple_rep(softmax, len(self.keys))
self.other = ensure_tuple_rep(other, len(self.keys))
self.converter = Activations()
def __init__(self,
keys: KeysCollection,
sigmoid=False,
softmax=False,
other=None):
"""
Args:
keys: keys of the corresponding items to model output and label.
See also: :py:class:`monai.transforms.compose.MapTransform`
sigmoid (bool, tuple or list of bool): whether to execute sigmoid function on model
output before transform.
softmax (bool, tuple or list of bool): whether to execute softmax function on model
output before transform.
other (Callable, tuple or list of Callables): callable function to execute other activation layers,
for example: `other = lambda x: torch.tanh(x)`
"""
super().__init__(keys)
self.sigmoid = ensure_tuple_rep(sigmoid, len(self.keys))
self.softmax = ensure_tuple_rep(softmax, len(self.keys))
self.other = ensure_tuple_rep(other, len(self.keys))
self.converter = Activations()
