Remove (bool) from docstring, add type hints

Co-authored-by: Benjamin Gorman <[email protected]>

Author: 2 people

monai/data/csv_saver.py

@@ -31,7 +31,7 @@ def __init__(self, output_dir: str = "./", filename: str = "predictions.csv", ov
         Args:
             output_dir (str): output CSV file directory.
             filename (str): name of the saved CSV file name.
-            overwrite (bool): whether to overwriting existing CSV file content. If we are not overwriting,
+            overwrite: whether to overwriting existing CSV file content. If we are not overwriting,
                 then we check if the results have been previously saved, and load them to the prediction_dict.
 
         """

monai/data/dataloader.py

@@ -25,7 +25,7 @@ class DataLoader(torch.utils.data.DataLoader):
         dataset (Dataset): dataset from which to load the data.
         batch_size: how many samples per batch to load
             (default: ``1``).
-        shuffle (bool, optional): set to ``True`` to have the data reshuffled
+        shuffle: set to ``True`` to have the data reshuffled
             at every epoch (default: ``False``).
         sampler (Sampler, optional): defines the strategy to draw samples from
             the dataset. If specified, :attr:`shuffle` must be ``False``.
@@ -35,11 +35,11 @@ class DataLoader(torch.utils.data.DataLoader):
         num_workers: how many subprocesses to use for data
             loading. ``0`` means that the data will be loaded in the main process.
             (default: ``0``)
-        pin_memory (bool, optional): If ``True``, the data loader will copy Tensors
+        pin_memory: If ``True``, the data loader will copy Tensors
             into CUDA pinned memory before returning them.  If your data elements
             are a custom type, or your :attr:`collate_fn` returns a batch that is a custom type,
             see the example below.
-        drop_last (bool, optional): set to ``True`` to drop the last incomplete batch,
+        drop_last: set to ``True`` to drop the last incomplete batch,
             if the dataset size is not divisible by the batch size. If ``False`` and
             the size of dataset is not divisible by the batch size, then the last batch
             will be smaller. (default: ``False``)
@@ -53,7 +53,7 @@ def __init__(
         self,
         dataset,
         batch_size: Optional[int] = 1,
-        shuffle=False,
+        shuffle: bool = False,
         sampler=None,
         batch_sampler=None,
         num_workers: Optional[int] = 0,

monai/data/decathalon_datalist.py

@@ -37,15 +37,17 @@ def _append_paths(base_dir, is_segmentation, items):
     return items
 
 
-def load_decathalon_datalist(data_list_file_path, is_segmentation=True, data_list_key="training", base_dir=None):
+def load_decathalon_datalist(
+    data_list_file_path, is_segmentation: bool = True, data_list_key="training", base_dir=None
+):
     """Load image/label paths of decathalon challenge from JSON file
 
     Json file is similar to what you get from http://medicaldecathlon.com/
     Those dataset.json files
 
     Args:
         data_list_file_path (str): the path to the json file of datalist.
-        is_segmentation (bool): whether the datalist is for segmentation task, default is True.
+        is_segmentation: whether the datalist is for segmentation task, default is True.
         data_list_key (str): the key to get a list of dictionary to be used, default is "training".
         base_dir (str): the base directory of the dataset, if None, use the datalist directory.
 

monai/data/nifti_reader.py

@@ -42,10 +42,10 @@ def __init__(
             image_files (list of str): list of image filenames
             seg_files (list of str): if in segmentation task, list of segmentation filenames
             labels (list or array): if in classification task, list of classification labels
-            as_closest_canonical (bool): if True, load the image as closest to canonical orientation
+            as_closest_canonical: if True, load the image as closest to canonical orientation
             transform (Callable, optional): transform to apply to image arrays
             seg_transform (Callable, optional): transform to apply to segmentation arrays
-            image_only (bool): if True return only the image volume, other return image volume and header dict
+            image_only: if True return only the image volume, other return image volume and header dict
             dtype (np.dtype, optional): if not None convert the loaded image to this data type
         """
 

monai/data/nifti_saver.py

@@ -41,7 +41,7 @@ def __init__(
             output_dir (str): output image directory.
             output_postfix (str): a string appended to all output file names.
             output_ext (str): output file extension name.
-            resample (bool): whether to resample before saving the data array.
+            resample: whether to resample before saving the data array.
             interp_order: the order of the spline interpolation, default is InterpolationCode.SPLINE3.
                 The order has to be in the range 0 - 5.
                 https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.affine_transform.html

monai/data/nifti_writer.py

@@ -21,7 +21,7 @@ def write_nifti(
     file_name,
     affine=None,
     target_affine=None,
-    resample=True,
+    resample: bool = True,
     output_shape=None,
     interp_order=InterpolationCode.SPLINE3,
     mode="constant",
@@ -63,7 +63,7 @@ def write_nifti(
         target_affine (numpy.ndarray, optional): before saving
             the (`data`, `affine`) as a Nifti1Image,
             transform the data into the coordinates defined by `target_affine`.
-        resample (bool): whether to run resampling when the target affine
+        resample: whether to run resampling when the target affine
             could not be achieved by swapping/flipping data axes.
         output_shape (None or tuple of ints): output image shape.
             this option is used when resample = True.
@@ -126,8 +126,8 @@ def write_nifti(
                     cval=cval,
                 )
             )
-        data_chns = np.stack(data_chns, axis=-1)
-        data_ = data_chns.reshape(list(data_chns.shape[:3]) + list(channel_shape))
+        data_chns_ = np.stack(data_chns, axis=-1)
+        data_ = data_chns_.reshape(list(data_chns_.shape[:3]) + list(channel_shape))
     else:
         data_ = data.astype(dtype)
         data_ = scipy.ndimage.affine_transform(

monai/data/png_saver.py

@@ -41,7 +41,7 @@ def __init__(
             output_dir (str): output image directory.
             output_postfix (str): a string appended to all output file names.
             output_ext (str): output file extension name.
-            resample (bool): whether to resample and resize if providing spatial_shape in the metadata.
+            resample: whether to resample and resize if providing spatial_shape in the metadata.
             interp_order: the order of the spline interpolation, default is InterpolationCode.SPLINE3.
                 This option is used when spatial_shape is specified and different from the data shape.
                 The order has to be in the range 0 - 5.
@@ -50,7 +50,7 @@ def __init__(
                 This option is used when spatial_shape is specified and different from the data shape.
             cval (scalar): Value to fill past edges of input if mode is "constant". Default is 0.0.
                 This option is used when spatial_shape is specified and different from the data shape.
-            scale (bool): whether to scale data with 255 and convert to uint8 for data in range [0, 1].
+            scale: whether to scale data with 255 and convert to uint8 for data in range [0, 1].
 
         """
         self.output_dir = output_dir

monai/data/png_writer.py

@@ -20,7 +20,7 @@ def write_png(
     interp_order: int = 3,
     mode="constant",
     cval=0,
-    scale=False,
+    scale: bool = False,
     plugin=None,
     **plugin_args,
 ):
@@ -41,7 +41,7 @@ def write_png(
             this option is used when `output_shape != None`.
         cval (scalar): Value to fill past edges of input if mode is "constant". Default is 0.0.
             this option is used when `output_shape != None`.
-        scale (bool): whether to scale data with 255 and convert to uint8 for data in range [0, 1].
+        scale: whether to scale data with 255 and convert to uint8 for data in range [0, 1].
         plugin (string): name of plugin to use in `imsave`. By default, the different plugins
             are tried(starting with imageio) until a suitable candidate is found.
         plugin_args (keywords): arguments passed to the given plugin.

monai/data/utils.py

@@ -157,7 +157,7 @@ def iter_patch(
         arr (np.ndarray): array to iterate over
         patch_size (tuple of int or None): size of patches to generate slices for, 0 or None selects whole dimension
         start_pos (tuple of it, optional): starting position in the array, default is 0 for each dimension
-        copy_back (bool): if True data from the yielded patches is copied back to `arr` once the generator completes
+        copy_back: if True data from the yielded patches is copied back to `arr` once the generator completes
         pad_mode (str, optional): padding mode, see `numpy.pad`
         pad_opts (dict, optional): padding options, see `numpy.pad`
 
@@ -304,7 +304,7 @@ def zoom_affine(affine, scale, diagonal: bool = True):
     Args:
         affine (nxn matrix): a square matrix.
         scale (sequence of floats): new scaling factor along each dimension.
-        diagonal (bool): whether to return a diagonal scaling matrix.
+        diagonal: whether to return a diagonal scaling matrix.
             Defaults to True.
 
     returns:

monai/engines/multi_gpu_supervised_trainer.py

@@ -44,7 +44,7 @@ def create_multigpu_supervised_trainer(
         loss_fn (`torch.nn` loss function): the loss function to use.
         devices (list, optional): device(s) type specification (default: None).
             Applies to both model and batches. None is all devices used, empty list is CPU only.
-        non_blocking (bool, optional): if True and this copy is between CPU and GPU, the copy may occur asynchronously
+        non_blocking: if True and this copy is between CPU and GPU, the copy may occur asynchronously
             with respect to the host. For other cases, this argument has no effect.
         prepare_batch (callable, optional): function that receives `batch`, `device`, `non_blocking` and outputs
             tuple of tensors `(batch_x, batch_y)`.
@@ -85,7 +85,7 @@ def create_multigpu_supervised_evaluator(
         metrics (dict of str - :class:`~ignite.metrics.Metric`): a map of metric names to Metrics.
         devices (list, optional): device(s) type specification (default: None).
             Applies to both model and batches. None is all devices used, empty list is CPU only.
-        non_blocking (bool, optional): if True and this copy is between CPU and GPU, the copy may occur asynchronously
+        non_blocking: if True and this copy is between CPU and GPU, the copy may occur asynchronously
             with respect to the host. For other cases, this argument has no effect.
         prepare_batch (callable, optional): function that receives `batch`, `device`, `non_blocking` and outputs
             tuple of tensors `(batch_x, batch_y)`.

monai/engines/trainer.py

@@ -56,7 +56,7 @@ class SupervisedTrainer(Trainer):
             and `batchdata` as input parameters. if not provided, use `self._iteration()` instead.
         lr_scheduler (LR Scheduler): the lr scheduler associated to the optimizer.
         inferer (Inferer): inference method that execute model forward on input data, like: SlidingWindow, etc.
-        amp (bool): whether to enable auto-mixed-precision training, reserved.
+        amp: whether to enable auto-mixed-precision training, reserved.
         post_transform (Transform): execute additional transformation for the model output data.
             Typically, several Tensor based transforms composed by `Compose`.
         key_train_metric (ignite.metric): compute metric when every iteration completed, and save average value to

monai/engines/workflow.py

@@ -29,7 +29,7 @@ class Workflow(ABC, Engine):
     Args:
         device (torch.device): an object representing the device on which to run.
         max_epochs: the total epoch number for engine to run, validator and evaluator have only 1 epoch.
-        amp (bool): whether to enable auto-mixed-precision training, reserved.
+        amp: whether to enable auto-mixed-precision training, reserved.
         data_loader (torch.DataLoader): Ignite engine use data_loader to run, must be torch.DataLoader.
         prepare_batch (Callable): function to parse image and label for every iteration.
         iteration_update (Callable): the callable function for every iteration, expect to accept `engine`
@@ -49,7 +49,7 @@ def __init__(
         self,
         device,
         max_epochs: int,
-        amp,
+        amp: bool,
         data_loader,
         prepare_batch=default_prepare_batch,
         iteration_update=None,

monai/handlers/checkpoint_saver.py

@@ -30,15 +30,15 @@ class CheckpointSaver:
 
         name (str): identifier of logging.logger to use, if None, defaulting to ``engine.logger``.
         file_prefix (str): prefix for the filenames to which objects will be saved.
-        save_final (bool): whether to save checkpoint or session at final iteration or exception.
-        save_key_metric (bool): whether to save checkpoint or session when the value of key_metric is
+        save_final: whether to save checkpoint or session at final iteration or exception.
+        save_key_metric: whether to save checkpoint or session when the value of key_metric is
             higher than all the previous values during training.keep 4 decimal places of metric,
             checkpoint name is: {file_prefix}_key_metric=0.XXXX.pth.
         key_metric_name (str): the name of key_metric in ignite metrics dictionary.
             if None, use `engine.state.key_metric` instead.
         key_metric_n_saved: save top N checkpoints or sessions, sorted by the value of key
             metric in descending order.
-        epoch_level (bool): save checkpoint during training for every N epochs or every N iterations.
+        epoch_level: save checkpoint during training for every N epochs or every N iterations.
             `True` is epoch level, `False` is iteration level.
         save_interval: save checkpoint every N epochs, default is 0 to save no checkpoint.
         n_saved: save latest N checkpoints of epoch level or iteration level, 'None' is to save all.

monai/handlers/classification_saver.py

@@ -34,7 +34,7 @@ def __init__(
         Args:
             output_dir (str): output CSV file directory.
             filename (str): name of the saved CSV file name.
-            overwrite (bool): whether to overwriting existing CSV file content. If we are not overwriting,
+            overwrite: whether to overwriting existing CSV file content. If we are not overwriting,
                 then we check if the results have been previously saved, and load them to the prediction_dict.
             batch_transform (Callable): a callable that is used to transform the
                 ignite.engine.batch into expected format to extract the meta_data dictionary.

monai/handlers/lr_schedule_handler.py

@@ -33,9 +33,9 @@ def __init__(
         Args:
             lr_scheduler (torch.optim.lr_scheduler): typically, lr_scheduler should be PyTorch
                 lr_scheduler object. If customized version, must have `step` and `get_last_lr` methods.
-            print_lr (bool): whether to print out the latest learning rate with logging.
+            print_lr: whether to print out the latest learning rate with logging.
             name (str): identifier of logging.logger to use, if None, defaulting to ``engine.logger``.
-            epoch_level (bool): execute lr_scheduler.step() after every epoch or every iteration.
+            epoch_level: execute lr_scheduler.step() after every epoch or every iteration.
                 `True` is epoch level, `False` is iteration level.
             step_transform (Callable): a callable that is used to transform the information from `engine`
                 to expected input data of lr_scheduler.step() function if necessary.

monai/handlers/mean_dice.py

@@ -37,12 +37,12 @@ def __init__(
         """
 
         Args:
-            include_background (Bool): whether to include dice computation on the first channel of the predicted output.
+            include_background: whether to include dice computation on the first channel of the predicted output.
                 Defaults to True.
-            to_onehot_y (Bool): whether to convert the output prediction into the one-hot format. Defaults to False.
-            mutually_exclusive (Bool): if True, the output prediction will be converted into a binary matrix using
+            to_onehot_y: whether to convert the output prediction into the one-hot format. Defaults to False.
+            mutually_exclusive: if True, the output prediction will be converted into a binary matrix using
                 a combination of argmax and to_onehot. Defaults to False.
-            sigmoid (Bool): whether to add sigmoid function to the output prediction before computing Dice.
+            sigmoid: whether to add sigmoid function to the output prediction before computing Dice.
                 Defaults to False.
             logit_thresh (Float): the threshold value to round value to 0.0 and 1.0. Defaults to None (no thresholding).
             output_transform (Callable): transform the ignite.engine.state.output into [y_pred, y] pair.

monai/handlers/segmentation_saver.py

@@ -42,7 +42,7 @@ def __init__(
             output_dir (str): output image directory.
             output_postfix (str): a string appended to all output file names.
             output_ext (str): output file extension name.
-            resample (bool): whether to resample before saving the data array.
+            resample: whether to resample before saving the data array.
             interp_order: the order of the spline interpolation, default is 0.
                 The order has to be in the range 0 - 5.
                 https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.affine_transform.html
@@ -52,7 +52,7 @@ def __init__(
                 This option is used when `resample = True`.
             cval (scalar): Value to fill past edges of input if mode is "constant". Default is 0.0.
                 This option is used when `resample = True`.
-            scale (bool): whether to scale data with 255 and convert to uint8 for data in range [0, 1].
+            scale: whether to scale data with 255 and convert to uint8 for data in range [0, 1].
                 It's used for PNG format only.
             dtype (np.dtype, optional): convert the image data to save to this data type.
                 If None, keep the original type of data. It's used for Nifti format only.

monai/handlers/tensorboard_handlers.py

@@ -192,7 +192,7 @@ def __init__(
         summary_writer: Optional[SummaryWriter] = None,
         log_dir: str = "./runs",
         interval: int = 1,
-        epoch_level=True,
+        epoch_level: bool = True,
         batch_transform: Callable = lambda x: x,
         output_transform: Callable = lambda x: x,
         global_iter_transform: Callable = lambda x: x,
@@ -206,7 +206,7 @@ def __init__(
                 default to create a new writer.
             log_dir (str): if using default SummaryWriter, write logs to this directory, default is `./runs`.
             interval: plot content from engine.state every N epochs or every N iterations, default is 1.
-            epoch_level (bool): plot content from engine.state every N epochs or N iterations. `True` is epoch level,
+            epoch_level: plot content from engine.state every N epochs or N iterations. `True` is epoch level,
                 `False` is iteration level.
             batch_transform (Callable): a callable that is used to transform the
                 ``ignite.engine.batch`` into expected format to extract several label data.

monai/handlers/validation_handler.py

@@ -25,7 +25,7 @@ def __init__(self, validator: Evaluator, interval: int, epoch_level: bool = True
         Args:
             validator (Evaluator): run the validator when trigger validation, suppose to be Evaluator.
             interval: do validation every N epochs or every N iterations during training.
-            epoch_level (bool): execute validation every N epochs or N iterations.
+            epoch_level: execute validation every N epochs or N iterations.
                 `True` is epoch level, `False` is iteration level.
 
         """