auto updates (#5321)

Signed-off-by: monai-bot <[email protected]>

Signed-off-by: monai-bot <[email protected]>

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`.
 

monai/metrics/meandice.py

@@ -84,7 +84,7 @@ def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor):  # type: ignor
             y_pred=y_pred, y=y, include_background=self.include_background, ignore_empty=self.ignore_empty
         )
 
-    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_meandice`.
 

monai/metrics/meaniou.py

@@ -84,7 +84,7 @@ def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor):  # type: ignor
             y_pred=y_pred, y=y, include_background=self.include_background, ignore_empty=self.ignore_empty
         )
 
-    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_meaniou`.
 

monai/metrics/metric.py

@@ -45,7 +45,7 @@ class IterationMetric(Metric):
     Subclasses typically implement the `_compute_tensor` function for the actual tensor computation logic.
     """
 
-    def __call__(self, y_pred: TensorOrList, y: Optional[TensorOrList] = None):  # type: ignore
+    def __call__(self, y_pred: TensorOrList, y: Optional[TensorOrList] = None):
         """
         Execute basic computation for model prediction `y_pred` and ground truth `y` (optional).
         It supports inputs of a list of "channel-first" Tensor and a "batch-first" Tensor.
@@ -310,7 +310,7 @@ class CumulativeIterationMetric(Cumulative, IterationMetric):
 
     """
 
-    def __call__(self, y_pred: TensorOrList, y: Optional[TensorOrList] = None):  # type: ignore
+    def __call__(self, y_pred: TensorOrList, y: Optional[TensorOrList] = None):
         """
         Execute basic computation for model prediction and ground truth.
         It can support  both `list of channel-first Tensor` and `batch-first Tensor`.

monai/metrics/regression.py

@@ -48,7 +48,7 @@ def __init__(
         self.reduction = reduction
         self.get_not_nans = get_not_nans
 
-    def aggregate(self, reduction: Union[MetricReduction, str, None] = None):  # type: ignore
+    def aggregate(self, reduction: Union[MetricReduction, str, None] = None):
         """
         Args:
             reduction: define mode of reduction to the metrics, will only apply reduction on `not-nan` values,

monai/metrics/rocauc.py

@@ -51,7 +51,7 @@ def __init__(self, average: Union[Average, str] = Average.MACRO) -> None:
     def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor):  # type: ignore
         return y_pred, y
 
-    def aggregate(self, average: Union[Average, str, None] = None):  # type: ignore
+    def aggregate(self, average: Union[Average, str, None] = None):
         """
         Typically `y_pred` and `y` are stored in the cumulative buffers at each iteration,
         This function reads the buffers and computes the area under the ROC.

monai/metrics/surface_dice.py

@@ -86,7 +86,7 @@ def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor):  # type: ignor
             distance_metric=self.distance_metric,
         )
 
-    def aggregate(self, reduction: Union[MetricReduction, str, None] = None):  # type: ignore
+    def aggregate(self, reduction: Union[MetricReduction, str, None] = None):
         r"""
         Aggregates the output of `_compute_tensor`.
 

monai/metrics/surface_distance.py

@@ -94,7 +94,7 @@ def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor):  # type: ignor
             distance_metric=self.distance_metric,
         )
 
-    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_average_surface_distance`.
 

monai/networks/layers/simplelayers.py

@@ -497,7 +497,8 @@ def median_filter(
     else:
         kernel = kernel.to(in_tensor)
     # map the local window to single vector
-    conv = [F.conv1d, F.conv2d, F.conv3d][spatial_dims - 1]  # type: ignore
+    conv = [F.conv1d, F.conv2d, F.conv3d][spatial_dims - 1]
+
     if "padding" not in kwargs:
         if pytorch_after(1, 10):
             kwargs["padding"] = "same"