Replace pyupgrade with builtin Ruff's UP rule

.pre-commit-config.yaml

@@ -30,22 +30,11 @@ repos:
     rev: v0.7.0
     hooks:
     -   id: ruff
-        args:
-        - --fix
-
-  - repo: https://github.com/asottile/pyupgrade
-    rev: v3.19.0
-    hooks:
-      - id: pyupgrade
-        args: [--py39-plus, --keep-runtime-typing]
-        name: Upgrade code with exceptions
+        args: ["--fix"]
         exclude: |
           (?x)(
               ^versioneer.py|
-              ^monai/_version.py|
-              ^monai/networks/| # avoid typing rewrites
-              ^monai/apps/detection/utils/anchor_utils.py| # avoid typing rewrites
-              ^tests/test_compute_panoptic_quality.py # avoid typing rewrites
+              ^monai/_version.py
           )
 
   - repo: https://github.com/asottile/yesqa

monai/apps/auto3dseg/bundle_gen.py

@@ -396,7 +396,7 @@ def _download_algos_url(url: str, at_path: str) -> dict[str, dict[str, str]]:
         try:
             download_and_extract(url=url, filepath=algo_compressed_file, output_dir=os.path.dirname(at_path))
         except Exception as e:
-            msg = f"Download and extract of {url} failed, attempt {i+1}/{download_attempts}."
+            msg = f"Download and extract of {url} failed, attempt {i + 1}/{download_attempts}."
             if i < download_attempts - 1:
                 warnings.warn(msg)
                 time.sleep(i)

monai/apps/deepgrow/dataset.py

@@ -201,14 +201,9 @@ 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};"
+        f" Label Shape: {vol_label.shape if vol_label is not None else None} => {label_count};"
+        f" Unique Labels: {unique_labels_count}"
     )
     return data_list
 
@@ -259,13 +254,8 @@ 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};"
+        f" Label Shape: {vol_label.shape if vol_label is not None else None} => {label_count};"
+        f" Unique Labels: {unique_labels_count}"
     )
     return data_list

monai/apps/detection/networks/retinanet_detector.py

@@ -787,7 +787,7 @@ def compute_anchor_matched_idxs(
                 )
 
             if self.debug:
-                print(f"Max box overlap between anchors and gt boxes: {torch.max(match_quality_matrix,dim=1)[0]}.")
+                print(f"Max box overlap between anchors and gt boxes: {torch.max(match_quality_matrix, dim=1)[0]}.")
 
             if torch.max(matched_idxs_per_image) < 0:
                 warnings.warn(

monai/apps/detection/utils/anchor_utils.py

@@ -39,7 +39,7 @@
 
 from __future__ import annotations
 
-from typing import List, Sequence
+from collections.abc import Sequence
 
 import torch
 from torch import Tensor, nn
@@ -106,7 +106,7 @@ class AnchorGenerator(nn.Module):
             anchor_generator = AnchorGenerator(sizes, aspect_ratios)
     """
 
-    __annotations__ = {"cell_anchors": List[torch.Tensor]}
+    __annotations__ = {"cell_anchors": list[torch.Tensor]}
 
     def __init__(
         self,
@@ -174,13 +174,13 @@ def generate_anchors(
         if (self.spatial_dims >= 3) and (len(aspect_ratios_t.shape) != 2):
             raise ValueError(
                 f"In {self.spatial_dims}-D image, aspect_ratios for each level should be \
-                {len(aspect_ratios_t.shape)-1}-D. But got aspect_ratios with shape {aspect_ratios_t.shape}."
+                {len(aspect_ratios_t.shape) - 1}-D. But got aspect_ratios with shape {aspect_ratios_t.shape}."
             )
 
         if (self.spatial_dims >= 3) and (aspect_ratios_t.shape[1] != self.spatial_dims - 1):
             raise ValueError(
                 f"In {self.spatial_dims}-D image, aspect_ratios for each level should has \
-                shape (_,{self.spatial_dims-1}). But got aspect_ratios with shape {aspect_ratios_t.shape}."
+                shape (_,{self.spatial_dims - 1}). But got aspect_ratios with shape {aspect_ratios_t.shape}."
             )
 
         # if 2d, w:h = 1:aspect_ratios
@@ -364,7 +364,7 @@ class AnchorGeneratorWithAnchorShape(AnchorGenerator):
             anchor_generator = AnchorGeneratorWithAnchorShape(feature_map_scales, base_anchor_shapes)
     """
 
-    __annotations__ = {"cell_anchors": List[torch.Tensor]}
+    __annotations__ = {"cell_anchors": list[torch.Tensor]}
 
     def __init__(
         self,

monai/apps/detection/utils/detector_utils.py

@@ -91,11 +91,11 @@ def check_training_targets(
             if boxes.numel() == 0:
                 warnings.warn(
                     f"Warning: Given target boxes has shape of {boxes.shape}. "
-                    f"The detector reshaped it with boxes = torch.reshape(boxes, [0, {2* spatial_dims}])."
+                    f"The detector reshaped it with boxes = torch.reshape(boxes, [0, {2 * spatial_dims}])."
                 )
             else:
                 raise ValueError(
-                    f"Expected target boxes to be a tensor of shape [N, {2* spatial_dims}], got {boxes.shape}.)."
+                    f"Expected target boxes to be a tensor of shape [N, {2 * spatial_dims}], got {boxes.shape}.)."
                 )
         if not torch.is_floating_point(boxes):
             raise ValueError(f"Expected target boxes to be a float tensor, got {boxes.dtype}.")

monai/apps/nnunet/nnunet_bundle.py

@@ -13,7 +13,7 @@
 import os
 import shutil
 from pathlib import Path
-from typing import Any, Optional, Union
+from typing import Any
 
 import numpy as np
 import torch
@@ -36,17 +36,17 @@
 
 
 def get_nnunet_trainer(
-    dataset_name_or_id: Union[str, int],
+    dataset_name_or_id: str | int,
     configuration: str,
-    fold: Union[int, str],
+    fold: int | str,
     trainer_class_name: str = "nnUNetTrainer",
     plans_identifier: str = "nnUNetPlans",
     use_compressed_data: bool = False,
     continue_training: bool = False,
     only_run_validation: bool = False,
     disable_checkpointing: bool = False,
     device: str = "cuda",
-    pretrained_model: Optional[str] = None,
+    pretrained_model: str | None = None,
 ) -> Any:  # type: ignore
     """
     Get the nnUNet trainer instance based on the provided configuration.
@@ -166,7 +166,7 @@ class ModelnnUNetWrapper(torch.nn.Module):
     restoring network architecture, and setting up the predictor for inference.
     """
 
-    def __init__(self, predictor: object, model_folder: Union[str, Path], model_name: str = "model.pt"):  # type: ignore
+    def __init__(self, predictor: object, model_folder: str | Path, model_name: str = "model.pt"):  # type: ignore
         super().__init__()
         self.predictor = predictor
 
@@ -294,7 +294,7 @@ def forward(self, x: MetaTensor) -> MetaTensor:
         return MetaTensor(out_tensor, meta=x.meta)
 
 
-def get_nnunet_monai_predictor(model_folder: Union[str, Path], model_name: str = "model.pt") -> ModelnnUNetWrapper:
+def get_nnunet_monai_predictor(model_folder: str | Path, model_name: str = "model.pt") -> ModelnnUNetWrapper:
     """
     Initializes and returns a `nnUNetMONAIModelWrapper` containing the corresponding `nnUNetPredictor`.
     The model folder should contain the following files, created during training:
@@ -426,9 +426,9 @@ def get_network_from_nnunet_plans(
     plans_file: str,
     dataset_file: str,
     configuration: str,
-    model_ckpt: Optional[str] = None,
+    model_ckpt: str | None = None,
     model_key_in_ckpt: str = "model",
-) -> Union[torch.nn.Module, Any]:
+) -> torch.nn.Module | Any:
     """
     Load and initialize a nnUNet network based on nnUNet plans and configuration.
 
@@ -518,7 +518,7 @@ def convert_monai_bundle_to_nnunet(nnunet_config: dict, bundle_root_folder: str,
     from nnunetv2.utilities.dataset_name_id_conversion import maybe_convert_to_dataset_name
 
     def subfiles(
-        folder: Union[str, Path], prefix: Optional[str] = None, suffix: Optional[str] = None, sort: bool = True
+        folder: str | Path, prefix: str | None = None, suffix: str | None = None, sort: bool = True
     ) -> list[str]:
         res = [
             i.name

monai/auto3dseg/analyzer.py

@@ -285,7 +285,7 @@ def __call__(self, data):
         d[self.stats_name] = report
 
         torch.set_grad_enabled(restore_grad_state)
-        logger.debug(f"Get image stats spent {time.time()-start}")
+        logger.debug(f"Get image stats spent {time.time() - start}")
         return d
 
 
@@ -366,7 +366,7 @@ def __call__(self, data: Mapping) -> dict:
         d[self.stats_name] = report
 
         torch.set_grad_enabled(restore_grad_state)
-        logger.debug(f"Get foreground image stats spent {time.time()-start}")
+        logger.debug(f"Get foreground image stats spent {time.time() - start}")
         return d
 
 
@@ -535,7 +535,7 @@ def __call__(self, data: Mapping[Hashable, MetaTensor]) -> dict[Hashable, MetaTe
         d[self.stats_name] = report  # type: ignore[assignment]
 
         torch.set_grad_enabled(restore_grad_state)
-        logger.debug(f"Get label stats spent {time.time()-start}")
+        logger.debug(f"Get label stats spent {time.time() - start}")
         return d  # type: ignore[return-value]
 
 
@@ -913,9 +913,11 @@ def __init__(
         for i, hist_params in enumerate(zip(self.hist_bins, self.hist_range)):
             _hist_bins, _hist_range = hist_params
             if not isinstance(_hist_bins, int) or _hist_bins < 0:
-                raise ValueError(f"Expected {i+1}. hist_bins value to be positive integer but got {_hist_bins}")
+                raise ValueError(f"Expected {i + 1}. hist_bins value to be positive integer but got {_hist_bins}")
             if not isinstance(_hist_range, list) or len(_hist_range) != 2:
-                raise ValueError(f"Expected {i+1}. hist_range values to be list of length 2 but received {_hist_range}")
+                raise ValueError(
+                    f"Expected {i + 1}. hist_range values to be list of length 2 but received {_hist_range}"
+                )
 
     def __call__(self, data: dict) -> dict:
         """

monai/data/wsi_reader.py

@@ -210,7 +210,9 @@ def get_valid_level(
                 # Set the default value if no resolution parameter is provided.
                 level = 0
             if level >= n_levels:
-                raise ValueError(f"The maximum level of this image is {n_levels-1} while level={level} is requested)!")
+                raise ValueError(
+                    f"The maximum level of this image is {n_levels - 1} while level={level} is requested)!"
+                )
 
         return level
 

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/losses/adversarial_loss.py

@@ -57,8 +57,7 @@ def __init__(
 
         if criterion.lower() not in list(AdversarialCriterions):
             raise ValueError(
-                "Unrecognised criterion entered for Adversarial Loss. Must be one in: %s"
-                % ", ".join(AdversarialCriterions)
+                f"Unrecognised criterion entered for Adversarial Loss. Must be one in: {', '.join(AdversarialCriterions)}"
             )
 
         # Depending on the criterion, a different activation layer is used.

monai/losses/dice.py

@@ -494,7 +494,7 @@ def __init__(
             raise ValueError(f"dist_matrix must be C x C, got {dist_matrix.shape[0]} x {dist_matrix.shape[1]}.")
 
         if weighting_mode not in ["default", "GDL"]:
-            raise ValueError("weighting_mode must be either 'default' or 'GDL, got %s." % weighting_mode)
+            raise ValueError(f"weighting_mode must be either 'default' or 'GDL', got {weighting_mode}.")
 
         self.m = dist_matrix
         if isinstance(self.m, np.ndarray):

monai/losses/ds_loss.py

@@ -11,8 +11,6 @@
 
 from __future__ import annotations
 
-from typing import Union
-
 import torch
 import torch.nn.functional as F
 from torch.nn.modules.loss import _Loss
@@ -70,7 +68,7 @@ def get_loss(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
             target = F.interpolate(target, size=input.shape[2:], mode=self.interp_mode)
         return self.loss(input, target)  # type: ignore[no-any-return]
 
-    def forward(self, input: Union[None, torch.Tensor, list[torch.Tensor]], target: torch.Tensor) -> torch.Tensor:
+    def forward(self, input: None | torch.Tensor | list[torch.Tensor], target: torch.Tensor) -> torch.Tensor:
         if isinstance(input, (list, tuple)):
             weights = self.get_weights(levels=len(input))
             loss = torch.tensor(0, dtype=torch.float, device=target.device)

monai/losses/focal_loss.py

@@ -13,7 +13,6 @@
 
 import warnings
 from collections.abc import Sequence
-from typing import Optional
 
 import torch
 import torch.nn.functional as F
@@ -153,7 +152,7 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
         if target.shape != input.shape:
             raise ValueError(f"ground truth has different shape ({target.shape}) from input ({input.shape})")
 
-        loss: Optional[torch.Tensor] = None
+        loss: torch.Tensor | None = None
         input = input.float()
         target = target.float()
         if self.use_softmax:
@@ -203,7 +202,7 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
 
 
 def softmax_focal_loss(
-    input: torch.Tensor, target: torch.Tensor, gamma: float = 2.0, alpha: Optional[float] = None
+    input: torch.Tensor, target: torch.Tensor, gamma: float = 2.0, alpha: float | None = None
 ) -> torch.Tensor:
     """
     FL(pt) = -alpha * (1 - pt)**gamma * log(pt)
@@ -225,7 +224,7 @@ def softmax_focal_loss(
 
 
 def sigmoid_focal_loss(
-    input: torch.Tensor, target: torch.Tensor, gamma: float = 2.0, alpha: Optional[float] = None
+    input: torch.Tensor, target: torch.Tensor, gamma: float = 2.0, alpha: float | None = None
 ) -> torch.Tensor:
     """
     FL(pt) = -alpha * (1 - pt)**gamma * log(pt)

monai/losses/perceptual.py

@@ -95,10 +95,8 @@ def __init__(
 
         if network_type.lower() not in list(PercetualNetworkType):
             raise ValueError(
-                "Unrecognised criterion entered for Adversarial Loss. Must be one in: %s"
-                % ", ".join(PercetualNetworkType)
+                f"Unrecognised criterion entered for Perceptual Loss. Must be one in: {', '.join(PercetualNetworkType)}"
             )
-
         if cache_dir:
             torch.hub.set_dir(cache_dir)
             # raise a warning that this may change the default cache dir for all torch.hub calls

monai/losses/spatial_mask.py

@@ -14,7 +14,7 @@
 import inspect
 import warnings
 from collections.abc import Callable
-from typing import Any, Optional
+from typing import Any
 
 import torch
 from torch.nn.modules.loss import _Loss
@@ -47,7 +47,7 @@ def __init__(
         if not callable(self.loss):
             raise ValueError("The loss function is not callable.")
 
-    def forward(self, input: torch.Tensor, target: torch.Tensor, mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+    def forward(self, input: torch.Tensor, target: torch.Tensor, mask: torch.Tensor | None = None) -> torch.Tensor:
         """
         Args:
             input: the shape should be BNH[WD].