apply pyupgrade

.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]}.")

monai/losses/focal_loss.py

@@ -67,7 +67,7 @@ def __init__(
                 fl(pred, grnd)
 
         """
-        super(FocalLoss, self).__init__(reduction=LossReduction(reduction).value)
+        super().__init__(reduction=LossReduction(reduction).value)
         self.include_background = include_background
         self.to_onehot_y = to_onehot_y
         self.gamma = gamma

monai/losses/image_dissimilarity.py

@@ -87,7 +87,7 @@ def __init__(
         .. deprecated:: 0.6.0
             ``ndim`` is deprecated, use ``spatial_dims``.
         """
-        super(LocalNormalizedCrossCorrelationLoss, self).__init__(reduction=LossReduction(reduction).value)
+        super().__init__(reduction=LossReduction(reduction).value)
 
         if ndim is not None:
             spatial_dims = ndim
@@ -196,7 +196,7 @@ def __init__(
             smooth_nr: a small constant added to the numerator to avoid nan.
             smooth_dr: a small constant added to the denominator to avoid nan.
         """
-        super(GlobalMutualInformationLoss, self).__init__(reduction=LossReduction(reduction).value)
+        super().__init__(reduction=LossReduction(reduction).value)
         if num_bins <= 0:
             raise ValueError("num_bins must > 0, got {num_bins}")
         bin_centers = torch.linspace(0.0, 1.0, num_bins)  # (num_bins,)

monai/losses/multi_scale.py

@@ -67,7 +67,7 @@ def __init__(
             scales: list of scalars or None, if None, do not apply any scaling.
             kernel: gaussian or cauchy.
         """
-        super(MultiScaleLoss, self).__init__(reduction=LossReduction(reduction).value)
+        super().__init__(reduction=LossReduction(reduction).value)
         if kernel not in kernel_fn_dict.keys():
             raise ValueError(f"got unsupported kernel type: {kernel}", "only support gaussian and cauchy")
         self.kernel_fn = kernel_fn_dict[kernel]

monai/metrics/regression.py

@@ -57,9 +57,7 @@ def aggregate(self):  # type: ignore
 
     def _check_shape(self, y_pred: torch.Tensor, y: torch.Tensor) -> None:
         if y_pred.shape != y.shape:
-            raise ValueError(
-                "y_pred and y shapes dont match, received y_pred: [{}] and y: [{}]".format(y_pred.shape, y.shape)
-            )
+            raise ValueError(f"y_pred and y shapes dont match, received y_pred: [{y_pred.shape}] and y: [{y.shape}]")
 
         # also check if there is atleast one non-batch dimension i.e. num_dims >= 2
         if len(y_pred.shape) < 2:

monai/networks/blocks/activation.py

@@ -122,7 +122,7 @@ class MemoryEfficientSwish(nn.Module):
     """
 
     def __init__(self, inplace: bool = False):
-        super(MemoryEfficientSwish, self).__init__()
+        super().__init__()
         # inplace only works when using torch.nn.functional.silu
         self.inplace = inplace
 
@@ -156,7 +156,7 @@ class Mish(nn.Module):
     """
 
     def __init__(self, inplace: bool = False):
-        super(Mish, self).__init__()
+        super().__init__()
         # inplace only works when using torch.nn.functional.mish
         self.inplace = inplace
 

monai/networks/blocks/crf.py

@@ -57,7 +57,7 @@ def __init__(
             compatibility_matrix: a matrix describing class compatibility,
                 should be NxN where N is the number of classes.
         """
-        super(CRF, self).__init__()
+        super().__init__()
         self.iterations = iterations
         self.bilateral_weight = bilateral_weight
         self.gaussian_weight = gaussian_weight

monai/networks/blocks/dynunet_block.py

@@ -47,7 +47,7 @@ def __init__(
         norm_name: Union[Tuple, str],
         dropout: Optional[Union[Tuple, str, float]] = None,
     ):
-        super(UnetResBlock, self).__init__()
+        super().__init__()
         self.conv1 = get_conv_layer(
             spatial_dims,
             in_channels,
@@ -126,7 +126,7 @@ def __init__(
         norm_name: Union[Tuple, str],
         dropout: Optional[Union[Tuple, str, float]] = None,
     ):
-        super(UnetBasicBlock, self).__init__()
+        super().__init__()
         self.conv1 = get_conv_layer(
             spatial_dims,
             in_channels,
@@ -188,7 +188,7 @@ def __init__(
         norm_name: Union[Tuple, str],
         dropout: Optional[Union[Tuple, str, float]] = None,
     ):
-        super(UnetUpBlock, self).__init__()
+        super().__init__()
         upsample_stride = upsample_kernel_size
         self.transp_conv = get_conv_layer(
             spatial_dims,
@@ -222,7 +222,7 @@ class UnetOutBlock(nn.Module):
     def __init__(
         self, spatial_dims: int, in_channels: int, out_channels: int, dropout: Optional[Union[Tuple, str, float]] = None
     ):
-        super(UnetOutBlock, self).__init__()
+        super().__init__()
         self.conv = get_conv_layer(
             spatial_dims, in_channels, out_channels, kernel_size=1, stride=1, dropout=dropout, bias=True, conv_only=True
         )

monai/networks/blocks/fcn.py

@@ -36,7 +36,7 @@ def __init__(self, inplanes: int, planes: int, ks: int = 7):
             planes: number of output channels.
             ks: kernel size for one dimension. Defaults to 7.
         """
-        super(GCN, self).__init__()
+        super().__init__()
 
         conv2d_type: Type[nn.Conv2d] = Conv[Conv.CONV, 2]
         self.conv_l1 = conv2d_type(in_channels=inplanes, out_channels=planes, kernel_size=(ks, 1), padding=(ks // 2, 0))
@@ -67,7 +67,7 @@ def __init__(self, planes: int):
         Args:
             planes: number of input channels.
         """
-        super(Refine, self).__init__()
+        super().__init__()
 
         relu_type: Type[nn.ReLU] = Act[Act.RELU]
         conv2d_type: Type[nn.Conv2d] = Conv[Conv.CONV, 2]
@@ -116,7 +116,7 @@ class FCN(nn.Module):
     def __init__(
         self, out_channels: int = 1, upsample_mode: str = "bilinear", pretrained: bool = True, progress: bool = True
     ):
-        super(FCN, self).__init__()
+        super().__init__()
 
         conv2d_type: Type[nn.Conv2d] = Conv[Conv.CONV, 2]
 
@@ -231,7 +231,7 @@ def __init__(
         pretrained: bool = True,
         progress: bool = True,
     ):
-        super(MCFCN, self).__init__(
+        super().__init__(
             out_channels=out_channels, upsample_mode=upsample_mode, pretrained=pretrained, progress=progress
         )
 
@@ -251,4 +251,4 @@ def forward(self, x: torch.Tensor):
             x: in shape (batch, in_channels, spatial_1, spatial_2).
         """
         x = self.init_proj(x)
-        return super(MCFCN, self).forward(x)
+        return super().forward(x)

monai/networks/blocks/localnet_block.py

@@ -90,7 +90,7 @@ def __init__(
         out_channels: int,
         kernel_size: Union[Sequence[int], int],
     ) -> None:
-        super(ResidualBlock, self).__init__()
+        super().__init__()
         if in_channels != out_channels:
             raise ValueError(
                 f"expecting in_channels == out_channels, " f"got in_channels={in_channels}, out_channels={out_channels}"
@@ -119,7 +119,7 @@ def __init__(
         in_channels: int,
         out_channels: int,
     ) -> None:
-        super(LocalNetResidualBlock, self).__init__()
+        super().__init__()
         if in_channels != out_channels:
             raise ValueError(
                 f"expecting in_channels == out_channels, " f"got in_channels={in_channels}, out_channels={out_channels}"
@@ -165,7 +165,7 @@ def __init__(
         Raises:
             NotImplementedError: when ``kernel_size`` is even
         """
-        super(LocalNetDownSampleBlock, self).__init__()
+        super().__init__()
         self.conv_block = get_conv_block(
             spatial_dims=spatial_dims, in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size
         )
@@ -225,7 +225,7 @@ def __init__(
         Raises:
             ValueError: when ``in_channels != 2 * out_channels``
         """
-        super(LocalNetUpSampleBlock, self).__init__()
+        super().__init__()
         self.deconv_block = get_deconv_block(
             spatial_dims=spatial_dims,
             in_channels=in_channels,
@@ -309,7 +309,7 @@ def __init__(
         act: activation type and arguments. Defaults to ReLU.
         kernel_initializer: kernel initializer. Defaults to None.
         """
-        super(LocalNetFeatureExtractorBlock, self).__init__()
+        super().__init__()
         self.conv_block = get_conv_block(
             spatial_dims=spatial_dims, in_channels=in_channels, out_channels=out_channels, act=act, norm=None
         )

monai/networks/blocks/patchembedding.py

@@ -62,7 +62,7 @@ def __init__(
 
         """
 
-        super(PatchEmbeddingBlock, self).__init__()
+        super().__init__()
 
         if not (0 <= dropout_rate <= 1):
             raise ValueError("dropout_rate should be between 0 and 1.")

monai/networks/blocks/regunet_block.py

@@ -100,7 +100,7 @@ def __init__(
             num_layers: number of layers inside the block
             kernel_size: kernel_size
         """
-        super(RegistrationResidualConvBlock, self).__init__()
+        super().__init__()
         self.num_layers = num_layers
         self.layers = nn.ModuleList(
             [
@@ -158,7 +158,7 @@ def __init__(
             channels: channels
             pooling: use MaxPool if True, strided conv if False
         """
-        super(RegistrationDownSampleBlock, self).__init__()
+        super().__init__()
         if pooling:
             self.layer = Pool[Pool.MAX, spatial_dims](kernel_size=2)
         else:
@@ -235,7 +235,7 @@ def __init__(
             kernel_initializer: kernel initializer
             activation: kernel activation function
         """
-        super(RegistrationExtractionBlock, self).__init__()
+        super().__init__()
         self.extract_levels = extract_levels
         self.max_level = max(extract_levels)
         self.layers = nn.ModuleList(

monai/networks/blocks/selfattention.py

@@ -37,7 +37,7 @@ def __init__(
 
         """
 
-        super(SABlock, self).__init__()
+        super().__init__()
 
         if not (0 <= dropout_rate <= 1):
             raise ValueError("dropout_rate should be between 0 and 1.")