auto updates (Project-MONAI#5321)

Signed-off-by: monai-bot <monai.miccai2019@gmail.com> Signed-off-by: monai-bot <monai.miccai2019@gmail.com>

Author: monai-bot

monai/apps/auto3dseg/bundle_gen.py

@@ -73,7 +73,7 @@ def __init__(self, template_path: str):
  # track records when filling template config: {"<config name>": {"<placeholder key>": value, ...}, ...}
  self.fill_records: dict = {}
 
- def set_data_stats(self, data_stats_files: str): # type: ignore
+ def set_data_stats(self, data_stats_files: str):
  """
  Set the data analysis report (generated by DataAnalyzer).
 
@@ -351,7 +351,7 @@ def __init__(self, algo_path: str = ".", algos=None, data_stats_filename=None, d
  self.data_src_cfg_filename = data_src_cfg_name
  self.history: List[Dict] = []
 
- def set_data_stats(self, data_stats_filename: str): # type: ignore
+ def set_data_stats(self, data_stats_filename: str):
  """
  Set the data stats filename
 
@@ -377,7 +377,7 @@ def get_data_src(self):
  """Get the data source filename"""
  return self.data_src_cfg_filename
 
- def get_history(self) -> List: # type: ignore
+ def get_history(self) -> List:
  """get the history of the bundleAlgo object with their names/identifiers"""
  return self.history
 

monai/apps/auto3dseg/ensemble_builder.py

@@ -176,7 +176,7 @@ def sort_score(self):
  scores = concat_val_to_np(self.algos, [AlgoEnsembleKeys.SCORE])
  return np.argsort(scores).tolist()
 
- def collect_algos(self, n_best: int = -1): # type: ignore
+ def collect_algos(self, n_best: int = -1):
  """
  Rank the algos by finding the top N (n_best) validation scores.
  """

monai/apps/auto3dseg/hpo_gen.py

@@ -154,7 +154,7 @@ def get_hyperparameters(self):
  warn("NNI is not detected. The code will continue to run without NNI.")
  return {}
 
- def update_params(self, params: dict): # type: ignore
+ def update_params(self, params: dict):
  """
  Translate the parameter from monai bundle to meet NNI requirements.
 
@@ -198,7 +198,7 @@ def set_score(self, acc):
  else:
  warn("NNI is not detected. The code will continue to run without NNI.")
 
- def run_algo(self, obj_filename: str, output_folder: str = ".", template_path=None) -> None: # type: ignore
+ def run_algo(self, obj_filename: str, output_folder: str = ".", template_path=None) -> None:
  """
  The python interface for NNI to run.
 
@@ -333,7 +333,7 @@ def __call__(self, trial, obj_filename: str, output_folder: str = ".", template_
  self.run_algo(obj_filename, output_folder, template_path)
  return self.acc
 
- def update_params(self, params: dict): # type: ignore
+ def update_params(self, params: dict):
  """
  Translate the parameter from monai bundle.
 
@@ -368,7 +368,7 @@ def generate(self, output_folder: str = ".") -> None:
  ConfigParser.export_config_file(self.params, write_path)
  logger.info(write_path)
 
- def run_algo(self, obj_filename: str, output_folder: str = ".", template_path=None) -> None: # type: ignore
+ def run_algo(self, obj_filename: str, output_folder: str = ".", template_path=None) -> None:
  """
  The python interface for NNI to run.
 

monai/auto3dseg/utils.py

@@ -177,7 +177,7 @@ def concat_val_to_np(
  elif ragged:
  return np.concatenate(np_list, **kwargs) # type: ignore
  else:
- return np.concatenate([np_list], **kwargs) # type: ignore
+ return np.concatenate([np_list], **kwargs)
 
 
 def concat_multikeys_to_dict(

monai/bundle/config_item.py

@@ -257,15 +257,15 @@ def resolve_args(self):
  """
  return {k: v for k, v in self.get_config().items() if k not in self.non_arg_keys}
 
- def is_disabled(self) -> bool: # type: ignore
+ def is_disabled(self) -> bool:
  """
  Utility function used in `instantiate()` to check whether to skip the instantiation.
 
  """
  _is_disabled = self.get_config().get("_disabled_", False)
  return _is_disabled.lower().strip() == "true" if isinstance(_is_disabled, str) else bool(_is_disabled)
 
- def instantiate(self, **kwargs) -> object: # type: ignore
+ def instantiate(self, **kwargs) -> object:
  """
  Instantiate component based on ``self.config`` content.
  The target component must be a `class` or a `function`, otherwise, return `None`.

monai/inferers/utils.py

@@ -276,7 +276,8 @@ def sliding_window_inference(
  final_output = dict(zip(dict_key, output_image_list))
  else:
  final_output = tuple(output_image_list) # type: ignore
- final_output = final_output[0] if is_tensor_output else final_output # type: ignore
+ final_output = final_output[0] if is_tensor_output else final_output
+
  if isinstance(inputs, MetaTensor):
  final_output = convert_to_dst_type(final_output, inputs)[0] # type: ignore
  return final_output

monai/metrics/active_learning_metrics.py

@@ -49,7 +49,7 @@ def __init__(
  self.scalar_reduction = scalar_reduction
  self.threshold = threshold
 
- def __call__(self, y_pred: Any) -> Any: # type: ignore
+ def __call__(self, y_pred: Any) -> Any:
  """
  Args:
  y_pred: Predicted segmentation, typically segmentation model output.
@@ -88,7 +88,7 @@ def __init__(self, include_background: bool = True, scalar_reduction: str = "sum
  self.include_background = include_background
  self.scalar_reduction = scalar_reduction
 
- def __call__(self, y_pred: Any, y: Any): # type: ignore
+ def __call__(self, y_pred: Any, y: Any):
  """
  Args:
  y_pred: Predicted segmentation, typically segmentation model output.

monai/metrics/confusion_matrix.py

@@ -100,9 +100,7 @@ def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor): # type: ignor
 
  return get_confusion_matrix(y_pred=y_pred, y=y, include_background=self.include_background)
 
- def aggregate( # type: ignore
- self, compute_sample: bool = False, reduction: Union[MetricReduction, str, None] = None
- ):
+ def aggregate(self, compute_sample: bool = False, reduction: Union[MetricReduction, str, None] = None):
  """
  Execute reduction for the confusion matrix values.
 

monai/metrics/generalized_dice.py

@@ -80,7 +80,7 @@ def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor): # type: ignor
  y_pred=y_pred, y=y, include_background=self.include_background, weight_type=self.weight_type
  )
 
- def aggregate(self, reduction: Union[MetricReduction, str, None] = None): # type: ignore
+ def aggregate(self, reduction: Union[MetricReduction, str, None] = None):
  """
  Execute reduction logic for the output of `compute_generalized_dice`.
 

monai/metrics/hausdorff_distance.py

@@ -104,7 +104,7 @@ def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor): # type: ignor
  directed=self.directed,
  )
 
- def aggregate(self, reduction: Union[MetricReduction, str, None] = None): # type: ignore
+ def aggregate(self, reduction: Union[MetricReduction, str, None] = None):
  """
  Execute reduction logic for the output of `compute_hausdorff_distance`.