apply pyupgrade (#3026)

* apply pyupgrade

Signed-off-by: Jirka <[email protected]>

* exclude monai/_version.py

Signed-off-by: Jirka <[email protected]>

Author: Borda

.pre-commit-config.yaml

@@ -22,12 +22,17 @@ repos:
         args: ['--maxkb=1024']
       - id: detect-private-key
 
-  #- repo: https://github.com/asottile/pyupgrade
-  #  rev: v2.23.2
-  #  hooks:
-  #    - id: pyupgrade
-  #      args: [--py36-plus]
-  #      name: Upgrade code
+  - repo: https://github.com/asottile/pyupgrade
+    rev: v2.27.0
+    hooks:
+      - id: pyupgrade
+        args: [--py36-plus]
+        name: Upgrade code
+        exclude: |
+          (?x)^(
+              versioneer.py|
+              monai/_version.py
+          )$
 
   #- repo: https://github.com/asottile/yesqa
   #  rev: v1.2.3

monai/apps/datasets.py

@@ -111,7 +111,7 @@ def _generate_data_list(self, dataset_dir: str) -> List[Dict]:
             ValueError: When ``section`` is not one of ["training", "validation", "test"].
 
         """
-        class_names = sorted((x for x in os.listdir(dataset_dir) if os.path.isdir(os.path.join(dataset_dir, x))))
+        class_names = sorted(x for x in os.listdir(dataset_dir) if os.path.isdir(os.path.join(dataset_dir, x)))
         self.num_class = len(class_names)
         image_files = [
             [

monai/apps/deepgrow/dataset.py

@@ -97,7 +97,7 @@ def create_dataset(
         image = os.path.abspath(image)
         label = os.path.abspath(label) if label else None
 
-        logging.info("Image: {}; Label: {}".format(image, label if label else None))
+        logging.info(f"Image: {image}; Label: {label if label else None}")
         data = transforms({image_key: image, label_key: label})
         if dimension == 2:
             data = _save_data_2d(
@@ -154,7 +154,7 @@ def _save_data_2d(vol_idx, vol_image, vol_label, dataset_dir, relative_path):
         if vol_label is not None and np.sum(label) == 0:
             continue
 
-        image_file_prefix = "vol_idx_{:0>4d}_slice_{:0>3d}".format(vol_idx, sid)
+        image_file_prefix = f"vol_idx_{vol_idx:0>4d}_slice_{sid:0>3d}"
         image_file = os.path.join(dataset_dir, "images", image_file_prefix)
         image_file += ".npy"
 
@@ -177,7 +177,7 @@ def _save_data_2d(vol_idx, vol_image, vol_label, dataset_dir, relative_path):
         unique_labels_count = max(unique_labels_count, len(unique_labels))
 
         for idx in unique_labels:
-            label_file_prefix = "{}_region_{:0>2d}".format(image_file_prefix, int(idx))
+            label_file_prefix = f"{image_file_prefix}_region_{int(idx):0>2d}"
             label_file = os.path.join(dataset_dir, "labels", label_file_prefix)
             label_file += ".npy"
 
@@ -226,7 +226,7 @@ def _save_data_3d(vol_idx, vol_image, vol_label, dataset_dir, relative_path):
     label_count = 0
     unique_labels_count = 0
 
-    image_file_prefix = "vol_idx_{:0>4d}".format(vol_idx)
+    image_file_prefix = f"vol_idx_{vol_idx:0>4d}"
     image_file = os.path.join(dataset_dir, "images", image_file_prefix)
     image_file += ".npy"
 
@@ -248,7 +248,7 @@ def _save_data_3d(vol_idx, vol_image, vol_label, dataset_dir, relative_path):
         unique_labels_count = max(unique_labels_count, len(unique_labels))
 
         for idx in unique_labels:
-            label_file_prefix = "{}_region_{:0>2d}".format(image_file_prefix, int(idx))
+            label_file_prefix = f"{image_file_prefix}_region_{int(idx):0>2d}"
             label_file = os.path.join(dataset_dir, "labels", label_file_prefix)
             label_file += ".npy"
 

monai/apps/utils.py

@@ -81,7 +81,7 @@ def update_to(self, b: int = 1, bsize: int = 1, tsize: Optional[int] = None):
             if not has_tqdm and progress:
                 warnings.warn("tqdm is not installed, will not show the downloading progress bar.")
             urlretrieve(url, filepath)
-    except (URLError, HTTPError, ContentTooShortError, IOError) as e:
+    except (URLError, HTTPError, ContentTooShortError, OSError) as e:
         print(f"Download failed from {url} to {filepath}.")
         raise e
 

monai/config/deviceconfig.py

@@ -122,7 +122,7 @@ def get_system_info() -> OrderedDict:
     elif output["System"] == "Darwin":
         _dict_append(output, "Mac version", lambda: platform.mac_ver()[0])
     else:
-        with open("/etc/os-release", "r") as rel_f:
+        with open("/etc/os-release") as rel_f:
             linux_ver = re.search(r'PRETTY_NAME="(.*)"', rel_f.read())
         if linux_ver:
             _dict_append(output, "Linux version", lambda: linux_ver.group(1))

monai/data/dataset.py

@@ -989,7 +989,7 @@ def __init__(self, datasets: Sequence, transform: Optional[Callable] = None) ->
         super().__init__(list(datasets), transform=transform)
 
     def __len__(self) -> int:
-        return min((len(dataset) for dataset in self.data))
+        return min(len(dataset) for dataset in self.data)
 
     def _transform(self, index: int):
         def to_list(x):

monai/data/dataset_summary.py

@@ -60,7 +60,7 @@ def __init__(
         self.image_key = image_key
         self.label_key = label_key
         if image_key:
-            self.meta_key = "{}_{}".format(image_key, meta_key_postfix)
+            self.meta_key = f"{image_key}_{meta_key_postfix}"
         self.all_meta_data: List = []
 
     def collect_meta_data(self):

monai/data/utils.py

@@ -1029,7 +1029,7 @@ def json_hashing(item) -> bytes:
     """
     # TODO: Find way to hash transforms content as part of the cache
     cache_key = hashlib.md5(json.dumps(item, sort_keys=True).encode("utf-8")).hexdigest()
-    return f"{cache_key}".encode("utf-8")
+    return f"{cache_key}".encode()
 
 
 def pickle_hashing(item, protocol=pickle.HIGHEST_PROTOCOL) -> bytes:
@@ -1044,7 +1044,7 @@ def pickle_hashing(item, protocol=pickle.HIGHEST_PROTOCOL) -> bytes:
 
     """
     cache_key = hashlib.md5(pickle.dumps(sorted_dict(item), protocol=protocol)).hexdigest()
-    return f"{cache_key}".encode("utf-8")
+    return f"{cache_key}".encode()
 
 
 def sorted_dict(item, key=None, reverse=False):

monai/losses/deform.py

@@ -65,7 +65,7 @@ def __init__(
                 - ``"mean"``: the sum of the output will be divided by the number of elements in the output.
                 - ``"sum"``: the output will be summed.
         """
-        super(BendingEnergyLoss, self).__init__(reduction=LossReduction(reduction).value)
+        super().__init__(reduction=LossReduction(reduction).value)
 
     def forward(self, pred: torch.Tensor) -> torch.Tensor:
         """

monai/losses/dice.py

@@ -419,7 +419,7 @@ def __init__(
                 wass_loss(pred_score, grnd)  # 0
 
         """
-        super(GeneralizedWassersteinDiceLoss, self).__init__(reduction=LossReduction(reduction).value)
+        super().__init__(reduction=LossReduction(reduction).value)
 
         if dist_matrix.shape[0] != dist_matrix.shape[1]:
             raise ValueError(f"dist_matrix must be C x C, got {dist_matrix.shape[0]} x {dist_matrix.shape[1]}.")