[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

monai/apps/deepgrow/dataset.py

@@ -201,14 +201,7 @@ def _save_data_2d(vol_idx, vol_image, vol_label, dataset_dir, relative_path):
         logging.warning(f"Unique labels {unique_labels_count} exceeds 20. Please check if this is correct.")
 
     logging.info(
-        "{} => Image Shape: {} => {}; Label Shape: {} => {}; Unique Labels: {}".format(
-            vol_idx,
-            vol_image.shape,
-            image_count,
-            vol_label.shape if vol_label is not None else None,
-            label_count,
-            unique_labels_count,
-        )
+        f"{vol_idx} => Image Shape: {vol_image.shape} => {image_count}; Label Shape: {vol_label.shape if vol_label is not None else None} => {label_count}; Unique Labels: {unique_labels_count}"
     )
     return data_list
 
@@ -259,13 +252,6 @@ def _save_data_3d(vol_idx, vol_image, vol_label, dataset_dir, relative_path):
         logging.warning(f"Unique labels {unique_labels_count} exceeds 20. Please check if this is correct.")
 
     logging.info(
-        "{} => Image Shape: {} => {}; Label Shape: {} => {}; Unique Labels: {}".format(
-            vol_idx,
-            vol_image.shape,
-            image_count,
-            vol_label.shape if vol_label is not None else None,
-            label_count,
-            unique_labels_count,
-        )
+        f"{vol_idx} => Image Shape: {vol_image.shape} => {image_count}; Label Shape: {vol_label.shape if vol_label is not None else None} => {label_count}; Unique Labels: {unique_labels_count}"
     )
     return data_list

monai/handlers/stats_handler.py

@@ -260,7 +260,7 @@ def _default_iteration_print(self, engine: Engine) -> None:
                         "ignoring non-scalar output in StatsHandler,"
                         " make sure `output_transform(engine.state.output)` returns"
                         " a scalar or dictionary of key and scalar pairs to avoid this warning."
-                        " {}:{}".format(name, type(value))
+                        f" {name}:{type(value)}"
                     )
                     continue  # not printing multi dimensional output
                 out_str += self.key_var_format.format(name, value.item() if isinstance(value, torch.Tensor) else value)
@@ -273,7 +273,7 @@ def _default_iteration_print(self, engine: Engine) -> None:
                 "ignoring non-scalar output in StatsHandler,"
                 " make sure `output_transform(engine.state.output)` returns"
                 " a scalar or a dictionary of key and scalar pairs to avoid this warning."
-                " {}".format(type(loss))
+                f" {type(loss)}"
             )
 
         if not out_str:

monai/handlers/tensorboard_handlers.py

@@ -257,7 +257,7 @@ def _default_iteration_writer(self, engine: Engine, writer: SummaryWriter | Summ
                         "ignoring non-scalar output in TensorBoardStatsHandler,"
                         " make sure `output_transform(engine.state.output)` returns"
                         " a scalar or dictionary of key and scalar pairs to avoid this warning."
-                        " {}:{}".format(name, type(value))
+                        f" {name}:{type(value)}"
                     )
                     continue  # not plot multi dimensional output
                 self._write_scalar(
@@ -280,7 +280,7 @@ def _default_iteration_writer(self, engine: Engine, writer: SummaryWriter | Summ
                 "ignoring non-scalar output in TensorBoardStatsHandler,"
                 " make sure `output_transform(engine.state.output)` returns"
                 " a scalar or a dictionary of key and scalar pairs to avoid this warning."
-                " {}".format(type(loss))
+                f" {type(loss)}"
             )
         writer.flush()
 

monai/metrics/utils.py

@@ -13,7 +13,7 @@
 
 import warnings
 from collections.abc import Iterable, Sequence
-from functools import lru_cache, partial
+from functools import partial, cache
 from types import ModuleType
 from typing import Any
 
@@ -465,7 +465,7 @@ def prepare_spacing(
 ENCODING_KERNEL = {2: [[8, 4], [2, 1]], 3: [[[128, 64], [32, 16]], [[8, 4], [2, 1]]]}
 
 
-@lru_cache(maxsize=None)
+@cache
 def _get_neighbour_code_to_normals_table(device=None):
     """
     returns a lookup table. For every binary neighbour code (2x2x2 neighbourhood = 8 neighbours = 8 bits = 256 codes)

tests/profile_subclass/profiling.py

@@ -63,9 +63,7 @@ def main():
 
         b_min, b_avg, b_med, b_std = bench(tensor_1, tensor_2)
         print(
-            "Type {} time (microseconds):  min: {}, avg: {}, median: {}, and std {}.".format(
-                t.__name__, (10**6 * b_min), (10**6) * b_avg, (10**6) * b_med, (10**6) * b_std
-            )
+            f"Type {t.__name__} time (microseconds):  min: {10**6 * b_min}, avg: {(10**6) * b_avg}, median: {(10**6) * b_med}, and std {(10**6) * b_std}."
         )