def __init__(
self,
keys: KeysCollection,
argmax: Union[Sequence[bool], bool] = False,
to_onehot: Union[Sequence[bool], bool] = False,
n_classes: Optional[Union[Sequence[int], int]] = None,
threshold_values: Union[Sequence[bool], bool] = False,
logit_thresh: Union[Sequence[float], float] = 0.5,
) -> None:
"""
Args:
keys: keys of the corresponding items to model output and label.
See also: :py:class:`monai.transforms.compose.MapTransform`
argmax: whether to execute argmax function on input data before transform.
it also can be a sequence of bool, each element corresponds to a key in ``keys``.
to_onehot: whether to convert input data into the one-hot format. Defaults to False.
it also can be a sequence of bool, each element corresponds to a key in ``keys``.
n_classes: the number of classes to convert to One-Hot format. it also can be a
sequence of int, each element corresponds to a key in ``keys``.
threshold_values: whether threshold the float value to int number 0 or 1, default is False.
it also can be a sequence of bool, each element corresponds to a key in ``keys``.
logit_thresh: the threshold value for thresholding operation, default is 0.5.
it also can be a sequence of float, each element corresponds to a key in ``keys``.
"""
super().__init__(keys)
self.argmax = ensure_tuple_rep(argmax, len(self.keys))
self.to_onehot = ensure_tuple_rep(to_onehot, len(self.keys))
self.n_classes = ensure_tuple_rep(n_classes, len(self.keys))
self.threshold_values = ensure_tuple_rep(threshold_values,
len(self.keys))
self.logit_thresh = ensure_tuple_rep(logit_thresh, len(self.keys))
self.converter = AsDiscrete()
def __init__(
self,
keys: KeysCollection,
output_postfix: str = "discreted",
argmax: bool = False,
to_onehot: bool = False,
n_classes: Optional[int] = None,
threshold_values: bool = False,
logit_thresh: float = 0.5,
):
"""
Args:
keys: keys of the corresponding items to model output and label.
See also: :py:class:`monai.transforms.compose.MapTransform`
output_postfix: 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 `discreted`,
the output data keys will be: `pred_discreted`, `label_discreted`.
if set to None, will replace the original data with the same key.
argmax: whether to execute argmax function on input data before transform.
to_onehot: whether to convert input data into the one-hot format. Defaults to False.
n_classes: the number of classes to convert to One-Hot format.
threshold_values: whether threshold the float value to int number 0 or 1, default is False.
logit_thresh: the threshold value for thresholding operation, default is 0.5.
"""
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.argmax = ensure_tuple_rep(argmax, len(self.keys))
self.to_onehot = ensure_tuple_rep(to_onehot, len(self.keys))
self.n_classes = ensure_tuple_rep(n_classes, len(self.keys))
self.threshold_values = ensure_tuple_rep(threshold_values, len(self.keys))
self.logit_thresh = ensure_tuple_rep(logit_thresh, len(self.keys))
self.converter = AsDiscrete()
def __init__(
self,
keys: KeysCollection,
argmax: bool = False,
to_onehot: bool = False,
n_classes: Optional[int] = None,
threshold_values: bool = False,
logit_thresh: float = 0.5,
):
"""
Args:
keys: keys of the corresponding items to model output and label.
See also: :py:class:`monai.transforms.compose.MapTransform`
argmax: whether to execute argmax function on input data before transform.
to_onehot: whether to convert input data into the one-hot format. Defaults to False.
n_classes: the number of classes to convert to One-Hot format.
threshold_values: whether threshold the float value to int number 0 or 1, default is False.
logit_thresh: the threshold value for thresholding operation, default is 0.5.
"""
super().__init__(keys)
self.argmax = ensure_tuple_rep(argmax, len(self.keys))
self.to_onehot = ensure_tuple_rep(to_onehot, len(self.keys))
self.n_classes = ensure_tuple_rep(n_classes, len(self.keys))
self.threshold_values = ensure_tuple_rep(threshold_values,
len(self.keys))
self.logit_thresh = ensure_tuple_rep(logit_thresh, len(self.keys))
self.converter = AsDiscrete()
def __init__(
self,
keys: KeysCollection,
argmax: Union[Sequence[bool], bool] = False,
to_onehot: Union[Sequence[bool], bool] = False,
num_classes: Optional[Union[Sequence[int], int]] = None,
threshold_values: Union[Sequence[bool], bool] = False,
logit_thresh: Union[Sequence[float], float] = 0.5,
rounding: Union[Sequence[Optional[str]], Optional[str]] = None,
allow_missing_keys: bool = False,
n_classes: Optional[int] = None,
) -> None:
"""
Args:
keys: keys of the corresponding items to model output and label.
See also: :py:class:`monai.transforms.compose.MapTransform`
argmax: whether to execute argmax function on input data before transform.
it also can be a sequence of bool, each element corresponds to a key in ``keys``.
to_onehot: whether to convert input data into the one-hot format. Defaults to False.
it also can be a sequence of bool, each element corresponds to a key in ``keys``.
num_classes: the number of classes to convert to One-Hot format. it also can be a
sequence of int, each element corresponds to a key in ``keys``.
threshold_values: whether threshold the float value to int number 0 or 1, default is False.
it also can be a sequence of bool, each element corresponds to a key in ``keys``.
logit_thresh: the threshold value for thresholding operation, default is 0.5.
it also can be a sequence of float, each element corresponds to a key in ``keys``.
rounding: if not None, round the data according to the specified option,
available options: ["torchrounding"]. it also can be a sequence of str or None,
each element corresponds to a key in ``keys``.
allow_missing_keys: don't raise exception if key is missing.
.. deprecated:: 0.6.0
``n_classes`` is deprecated, use ``num_classes`` instead.
"""
# in case the new num_classes is default but you still call deprecated n_classes
if n_classes is not None and num_classes is None:
num_classes = n_classes
super().__init__(keys, allow_missing_keys)
self.argmax = ensure_tuple_rep(argmax, len(self.keys))
self.to_onehot = ensure_tuple_rep(to_onehot, len(self.keys))
self.num_classes = ensure_tuple_rep(num_classes, len(self.keys))
self.threshold_values = ensure_tuple_rep(threshold_values,
len(self.keys))
self.logit_thresh = ensure_tuple_rep(logit_thresh, len(self.keys))
self.rounding = ensure_tuple_rep(rounding, len(self.keys))
self.converter = AsDiscrete()
def __init__(
self,
keys: KeysCollection,
argmax: Union[Sequence[bool], bool] = False,
to_onehot: Union[Sequence[Optional[int]], Optional[int]] = None,
threshold: Union[Sequence[Optional[float]], Optional[float]] = None,
rounding: Union[Sequence[Optional[str]], Optional[str]] = None,
allow_missing_keys: bool = False,
n_classes: Optional[Union[Sequence[int], int]] = None, # deprecated
num_classes: Optional[Union[Sequence[int], int]] = None, # deprecated
logit_thresh: Union[Sequence[float], float] = 0.5, # deprecated
threshold_values: Union[Sequence[bool], bool] = False, # deprecated
) -> None:
"""
Args:
keys: keys of the corresponding items to model output and label.
See also: :py:class:`monai.transforms.compose.MapTransform`
argmax: whether to execute argmax function on input data before transform.
it also can be a sequence of bool, each element corresponds to a key in ``keys``.
to_onehot: if not None, convert input data into the one-hot format with specified number of classes.
defaults to ``None``. it also can be a sequence, each element corresponds to a key in ``keys``.
threshold: if not None, threshold the float values to int number 0 or 1 with specified theashold value.
defaults to ``None``. it also can be a sequence, each element corresponds to a key in ``keys``.
rounding: if not None, round the data according to the specified option,
available options: ["torchrounding"]. it also can be a sequence of str or None,
each element corresponds to a key in ``keys``.
allow_missing_keys: don't raise exception if key is missing.
.. deprecated:: 0.6.0
``n_classes`` is deprecated, use ``to_onehot`` instead.
.. deprecated:: 0.7.0
``num_classes`` is deprecated, use ``to_onehot`` instead.
``logit_thresh`` is deprecated, use ``threshold`` instead.
``threshold_values`` is deprecated, use ``threshold`` instead.
"""
super().__init__(keys, allow_missing_keys)
self.argmax = ensure_tuple_rep(argmax, len(self.keys))
to_onehot_ = ensure_tuple_rep(to_onehot, len(self.keys))
num_classes = ensure_tuple_rep(num_classes, len(self.keys))
self.to_onehot = []
for flag, val in zip(to_onehot_, num_classes):
if isinstance(flag, bool):
warnings.warn("`to_onehot=True/False` is deprecated, please use `to_onehot=num_classes` instead.")
self.to_onehot.append(val if flag else None)
else:
self.to_onehot.append(flag)
threshold_ = ensure_tuple_rep(threshold, len(self.keys))
logit_thresh = ensure_tuple_rep(logit_thresh, len(self.keys))
self.threshold = []
for flag, val in zip(threshold_, logit_thresh):
if isinstance(flag, bool):
warnings.warn("`threshold_values=True/False` is deprecated, please use `threshold=value` instead.")
self.threshold.append(val if flag else None)
else:
self.threshold.append(flag)
self.rounding = ensure_tuple_rep(rounding, len(self.keys))
self.converter = AsDiscrete()
