Fix review comments (Project-MONAI#2678)

Signed-off-by: Sebastian Penhouet <sebastian.penhouet@airamed.de>

docs/source/transforms.rst

@@ -357,13 +357,13 @@ Post-processing
  :special-members: __call__
 
 `LabelFilter`
-"""""""""""""""""""""""""""""""
+"""""""""""""
 .. autoclass:: LabelFilter
  :members:
  :special-members: __call__
 
 `FillHoles`
-"""""""""""""""""""""""""""""""
+"""""""""""
 .. autoclass:: FillHoles
  :members:
  :special-members: __call__
@@ -968,13 +968,13 @@ Post-processing (Dict)
  :special-members: __call__
 
 `LabelFilterd`
-""""""""""""""""""""""""""""""""
+""""""""""""""
 .. autoclass:: LabelFilterd
  :members:
  :special-members: __call__
 
 `FillHolesd`
-""""""""""""""""""""""""""""""""
+""""""""""""
 .. autoclass:: FillHolesd
  :members:
  :special-members: __call__

monai/transforms/post/array.py

@@ -315,7 +315,7 @@ def __init__(self, applied_labels: Union[Sequence[int], int]) -> None:
  Initialize the LabelFilter class with the labels to filter on.
 
  Args:
- applied_labels (Union[Sequence[int], int]): Label(s) to filter on.
+ applied_labels: Label(s) to filter on.
  """
  self.applied_labels = ensure_tuple(applied_labels)
 
@@ -324,13 +324,13 @@ def __call__(self, img: NdarrayTensor) -> NdarrayTensor:
  Filter the image on the `applied_labels`.
 
  Args:
- img (NdarrayTensor): Pytorch tensor or numpy array of any shape.
+ img: Pytorch tensor or numpy array of any shape.
 
  Raises:
  NotImplementedError: The provided image was not a Pytorch Tensor or numpy array.
 
  Returns:
- Union[np.ndarray, torch.Tensor]: Pytorch tensor or numpy array of the same shape as the input.
+ Pytorch tensor or numpy array of the same shape as the input.
  """
  if isinstance(img, np.ndarray):
  return np.asarray(np.where(np.isin(img, self.applied_labels), img, 0))
@@ -388,11 +388,9 @@ def __init__(
  Initialize the connectivity and limit the labels for which holes are filled.
 
  Args:
- applied_labels (Optional[Union[Iterable[int], int]], optional): Labels for which to fill holes. Defaults to None,
- that is filling holes for all labels.
- connectivity (int, optional): Maximum number of orthogonal hops to consider a pixel/voxel as a neighbor.
- Accepted values are ranging from 1 to input.ndim. Defaults to a full
- connectivity of ``input.ndim``.
+ applied_labels: Labels for which to fill holes. Defaults to None, that is filling holes for all labels.
+ connectivity: Maximum number of orthogonal hops to consider a pixel/voxel as a neighbor.
+ Accepted values are ranging from 1 to input.ndim. Defaults to a full connectivity of ``input.ndim``.
  """
  super().__init__()
  self.applied_labels = ensure_tuple(applied_labels) if applied_labels else None
@@ -406,15 +404,13 @@ def __call__(self, img: NdarrayTensor) -> NdarrayTensor:
  The value 0 is assumed as background label.
 
  Args:
- img (NdarrayTensor): Pytorch Tensor or numpy array
- of shape [batch_size, num_channel, spatial_dim1[, spatial_dim2, ...]].
+ img: Pytorch Tensor or numpy array of shape [batch_size, num_channel, spatial_dim1[, spatial_dim2, ...]].
 
  Raises:
  NotImplementedError: The provided image was not a Pytorch Tensor or numpy array.
 
  Returns:
- Union[np.ndarray, torch.Tensor]: Pytorch Tensor or numpy array
- of shape [batch_size, num_channel, spatial_dim1[, spatial_dim2, ...]].
+ Pytorch Tensor or numpy array of shape [batch_size, num_channel, spatial_dim1[, spatial_dim2, ...]].
  """
  if isinstance(img, np.ndarray):
  channel_axis = 1

monai/transforms/post/dictionary.py

@@ -231,7 +231,7 @@ def __init__(
  Args:
  keys: keys of the corresponding items to be transformed.
  See also: :py:class:`monai.transforms.compose.MapTransform`
- applied_labels (Union[Sequence[int], int]): Label(s) to filter on.
+ applied_labels: Label(s) to filter on.
  allow_missing_keys: don't raise exception if key is missing.
 
  """

monai/transforms/utils.py

@@ -750,15 +750,15 @@ def fill_holes(
  Limiting the number of `applied_labels` results in a big decrease in processing time.
 
  Args:
- img_arr (np.ndarray): numpy array of shape [batch_size, spatial_dim1[, spatial_dim2, ...]].
- applied_labels (Optional[Iterable[int]], optional): Labels for which to fill holes. Defaults to None,
+ img_arr: numpy array of shape [batch_size, spatial_dim1[, spatial_dim2, ...]].
+ applied_labels: Labels for which to fill holes. Defaults to None,
  that is filling holes for all labels.
- connectivity (Optional[int], optional): connectivity (int, optional): Maximum number of orthogonal hops to
+ connectivity: connectivity (int, optional): Maximum number of orthogonal hops to
  consider a pixel/voxel as a neighbor. Accepted values are ranging from 1 to input.ndim.
  Defaults to a full connectivity of ``input.ndim``.
 
  Returns:
- np.ndarray: numpy array of shape [batch_size, spatial_dim1[, spatial_dim2, ...]].
+ numpy array of shape [batch_size, spatial_dim1[, spatial_dim2, ...]].
  """
  # Ignore batch dimension in structure (window for dilation steps)
  spatial_dims = img_arr.ndim - 1