auto updates (#7439)

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

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

Author: monai-bot

monai/apps/datasets.py

@@ -737,6 +737,7 @@ def get_dataset(self, folds: Sequence[int] | int, **dataset_params: Any) -> obje
         dataset_params_.update(dataset_params)
 
         class _NsplitsDataset(self.dataset_cls):  # type: ignore
+
             def _split_datalist(self, datalist: list[dict]) -> list[dict]:
                 data = partition_dataset(data=datalist, num_partitions=nfolds, shuffle=True, seed=seed)
                 return select_cross_validation_folds(partitions=data, folds=folds)

monai/apps/deepedit/transforms.py

@@ -34,6 +34,7 @@
 
 
 class DiscardAddGuidanced(MapTransform):
+
     def __init__(
         self,
         keys: KeysCollection,
@@ -84,6 +85,7 @@ def __call__(self, data: Mapping[Hashable, np.ndarray]) -> dict[Hashable, np.nda
 
 
 class NormalizeLabelsInDatasetd(MapTransform):
+
     def __init__(
         self, keys: KeysCollection, label_names: dict[str, int] | None = None, allow_missing_keys: bool = False
     ):
@@ -121,6 +123,7 @@ def __call__(self, data: Mapping[Hashable, np.ndarray]) -> dict[Hashable, np.nda
 
 
 class SingleLabelSelectiond(MapTransform):
+
     def __init__(
         self, keys: KeysCollection, label_names: Sequence[str] | None = None, allow_missing_keys: bool = False
     ):

monai/apps/detection/metrics/coco.py

@@ -72,6 +72,7 @@
 
 
 class COCOMetric:
+
     def __init__(
         self,
         classes: Sequence[str],

monai/apps/detection/utils/ATSS_matcher.py

@@ -164,6 +164,7 @@ def compute_matches(
 
 
 class ATSSMatcher(Matcher):
+
     def __init__(
         self,
         num_candidates: int = 4,

monai/apps/pathology/transforms/post/array.py

@@ -162,7 +162,7 @@ def __call__(self, prob_map: NdarrayOrTensor) -> NdarrayOrTensor:
         pred = label(pred)[0]
         if self.remove_small_objects is not None:
             pred = self.remove_small_objects(pred)
-        pred[pred > 0] = 1  # type: ignore
+        pred[pred > 0] = 1
 
         return convert_to_dst_type(pred, prob_map, dtype=self.dtype)[0]
 
@@ -338,7 +338,7 @@ def __call__(self, mask: NdarrayOrTensor, instance_border: NdarrayOrTensor) -> N
         instance_border = instance_border >= self.threshold  # uncertain area
 
         marker = mask - convert_to_dst_type(instance_border, mask)[0]  # certain foreground
-        marker[marker < 0] = 0  # type: ignore
+        marker[marker < 0] = 0
         marker = self.postprocess_fn(marker)
         marker = convert_to_numpy(marker)
 
@@ -634,7 +634,7 @@ def __call__(  # type: ignore
 
         seg_map_crop = convert_to_dst_type(seg_map_crop == instance_id, type_map_crop, dtype=bool)[0]
 
-        inst_type = type_map_crop[seg_map_crop]  # type: ignore
+        inst_type = type_map_crop[seg_map_crop]
         type_list, type_pixels = unique(inst_type, return_counts=True)
         type_list = list(zip(type_list, type_pixels))
         type_list = sorted(type_list, key=lambda x: x[1], reverse=True)

monai/data/dataset.py

@@ -1275,6 +1275,7 @@ def __len__(self) -> int:
         return min(len(dataset) for dataset in self.data)
 
     def _transform(self, index: int):
+
         def to_list(x):
             return list(x) if isinstance(x, (tuple, list)) else [x]
 

monai/fl/client/client_algo.py

@@ -57,6 +57,7 @@ def abort(self, extra: dict | None = None) -> None:
 
 
 class ClientAlgoStats(BaseClient):
+
     def get_data_stats(self, extra: dict | None = None) -> ExchangeObject:
         """
         Get summary statistics about the local data.

monai/handlers/ignite_metric.py

@@ -157,6 +157,7 @@ def attach(self, engine: Engine, name: str) -> None:  # type: ignore[override]
 
 @deprecated(since="1.2", removed="1.4", msg_suffix="Use IgniteMetricHandler instead of IgniteMetric.")
 class IgniteMetric(IgniteMetricHandler):
+
     def __init__(
         self,
         metric_fn: CumulativeIterationMetric | None = None,

monai/metrics/f_beta_score.py

@@ -22,6 +22,7 @@
 
 
 class FBetaScore(CumulativeIterationMetric):
+
     def __init__(
         self,
         beta: float = 1.0,

monai/networks/blocks/dynunet_block.py

@@ -245,6 +245,7 @@ def forward(self, inp, skip):
 
 
 class UnetOutBlock(nn.Module):
+
     def __init__(
         self, spatial_dims: int, in_channels: int, out_channels: int, dropout: tuple | str | float | None = None
     ):

monai/networks/blocks/localnet_block.py

@@ -72,6 +72,7 @@ def get_deconv_block(spatial_dims: int, in_channels: int, out_channels: int) ->
 
 
 class ResidualBlock(nn.Module):
+
     def __init__(
         self, spatial_dims: int, in_channels: int, out_channels: int, kernel_size: Sequence[int] | int
     ) -> None:
@@ -95,6 +96,7 @@ def forward(self, x) -> torch.Tensor:
 
 
 class LocalNetResidualBlock(nn.Module):
+
     def __init__(self, spatial_dims: int, in_channels: int, out_channels: int) -> None:
         super().__init__()
         if in_channels != out_channels:

monai/networks/blocks/pos_embed_utils.py

@@ -23,6 +23,7 @@
 
 # From PyTorch internals
 def _ntuple(n):
+
     def parse(x):
         if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
             return tuple(x)

monai/networks/layers/gmm.py

@@ -78,6 +78,7 @@ def apply(self, features):
 
 
 class _ApplyFunc(torch.autograd.Function):
+
     @staticmethod
     def forward(ctx, params, features, compiled_extension):
         return compiled_extension.apply(params, features)

monai/networks/layers/simplelayers.py

@@ -552,6 +552,7 @@ def forward(self, in_tensor: torch.Tensor, number_of_passes=1) -> torch.Tensor:
 
 
 class GaussianFilter(nn.Module):
+
     def __init__(
         self,
         spatial_dims: int,
@@ -607,6 +608,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
 
 class LLTMFunction(Function):
+
     @staticmethod
     def forward(ctx, input, weights, bias, old_h, old_cell):
         outputs = _C.lltm_forward(input, weights, bias, old_h, old_cell)

monai/networks/layers/spatial_transforms.py

@@ -33,6 +33,7 @@
 
 
 class _GridPull(torch.autograd.Function):
+
     @staticmethod
     def forward(ctx, input, grid, interpolation, bound, extrapolate):
         opt = (bound, interpolation, extrapolate)
@@ -132,6 +133,7 @@ def grid_pull(
 
 
 class _GridPush(torch.autograd.Function):
+
     @staticmethod
     def forward(ctx, input, grid, shape, interpolation, bound, extrapolate):
         opt = (bound, interpolation, extrapolate)
@@ -236,6 +238,7 @@ def grid_push(
 
 
 class _GridCount(torch.autograd.Function):
+
     @staticmethod
     def forward(ctx, grid, shape, interpolation, bound, extrapolate):
         opt = (bound, interpolation, extrapolate)
@@ -335,6 +338,7 @@ def grid_count(grid: torch.Tensor, shape=None, interpolation="linear", bound="ze
 
 
 class _GridGrad(torch.autograd.Function):
+
     @staticmethod
     def forward(ctx, input, grid, interpolation, bound, extrapolate):
         opt = (bound, interpolation, extrapolate)
@@ -433,6 +437,7 @@ def grid_grad(input: torch.Tensor, grid: torch.Tensor, interpolation="linear", b
 
 
 class AffineTransform(nn.Module):
+
     def __init__(
         self,
         spatial_size: Sequence[int] | int | None = None,

monai/networks/nets/ahnet.py

@@ -87,6 +87,7 @@ def forward(self, x):
 
 
 class Projection(nn.Sequential):
+
     def __init__(self, spatial_dims: int, num_input_features: int, num_output_features: int):
         super().__init__()
 
@@ -100,6 +101,7 @@ def __init__(self, spatial_dims: int, num_input_features: int, num_output_featur
 
 
 class DenseBlock(nn.Sequential):
+
     def __init__(
         self,
         spatial_dims: int,
@@ -118,6 +120,7 @@ def __init__(
 
 
 class UpTransition(nn.Sequential):
+
     def __init__(
         self, spatial_dims: int, num_input_features: int, num_output_features: int, upsample_mode: str = "transpose"
     ):
@@ -143,6 +146,7 @@ def __init__(
 
 
 class Final(nn.Sequential):
+
     def __init__(
         self, spatial_dims: int, num_input_features: int, num_output_features: int, upsample_mode: str = "transpose"
     ):
@@ -178,6 +182,7 @@ def __init__(
 
 
 class Pseudo3DLayer(nn.Module):
+
     def __init__(self, spatial_dims: int, num_input_features: int, growth_rate: int, bn_size: int, dropout_prob: float):
         super().__init__()
         # 1x1x1
@@ -244,6 +249,7 @@ def forward(self, x):
 
 
 class PSP(nn.Module):
+
     def __init__(self, spatial_dims: int, psp_block_num: int, in_ch: int, upsample_mode: str = "transpose"):
         super().__init__()
         self.up_modules = nn.ModuleList()

monai/networks/nets/attentionunet.py

@@ -23,6 +23,7 @@
 
 
 class ConvBlock(nn.Module):
+
     def __init__(
         self,
         spatial_dims: int,
@@ -67,6 +68,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
 
 class UpConv(nn.Module):
+
     def __init__(self, spatial_dims: int, in_channels: int, out_channels: int, kernel_size=3, strides=2, dropout=0.0):
         super().__init__()
         self.up = Convolution(
@@ -88,6 +90,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
 
 class AttentionBlock(nn.Module):
+
     def __init__(self, spatial_dims: int, f_int: int, f_g: int, f_l: int, dropout=0.0):
         super().__init__()
         self.W_g = nn.Sequential(
@@ -145,6 +148,7 @@ def forward(self, g: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
 
 
 class AttentionLayer(nn.Module):
+
     def __init__(
         self,
         spatial_dims: int,

monai/networks/nets/basic_unet.py

@@ -176,6 +176,7 @@ def forward(self, x: torch.Tensor, x_e: Optional[torch.Tensor]):
 
 
 class BasicUNet(nn.Module):
+
     def __init__(
         self,
         spatial_dims: int = 3,

monai/networks/nets/basic_unetplusplus.py

@@ -24,6 +24,7 @@
 
 
 class BasicUNetPlusPlus(nn.Module):
+
     def __init__(
         self,
         spatial_dims: int = 3,

monai/networks/nets/densenet.py

@@ -42,6 +42,7 @@
 
 
 class _DenseLayer(nn.Module):
+
     def __init__(
         self,
         spatial_dims: int,
@@ -88,6 +89,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
 
 class _DenseBlock(nn.Sequential):
+
     def __init__(
         self,
         spatial_dims: int,
@@ -119,6 +121,7 @@ def __init__(
 
 
 class _Transition(nn.Sequential):
+
     def __init__(
         self,
         spatial_dims: int,

monai/networks/nets/dints.py

@@ -73,6 +73,7 @@ def _dfs(node, paths):
 
 
 class _IdentityWithRAMCost(nn.Identity):
+
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.ram_cost = 0
@@ -105,6 +106,7 @@ def __init__(
 
 
 class _P3DActiConvNormBlockWithRAMCost(P3DActiConvNormBlock):
+
     def __init__(
         self,
         in_channel: int,
@@ -122,6 +124,7 @@ def __init__(
 
 
 class _FactorizedIncreaseBlockWithRAMCost(FactorizedIncreaseBlock):
+
     def __init__(
         self,
         in_channel: int,
@@ -138,6 +141,7 @@ def __init__(
 
 
 class _FactorizedReduceBlockWithRAMCost(FactorizedReduceBlock):
+
     def __init__(
         self,
         in_channel: int,

monai/networks/nets/efficientnet.py

@@ -73,6 +73,7 @@
 
 
 class MBConvBlock(nn.Module):
+
     def __init__(
         self,
         spatial_dims: int,
@@ -227,6 +228,7 @@ def set_swish(self, memory_efficient: bool = True) -> None:
 
 
 class EfficientNet(nn.Module):
+
     def __init__(
         self,
         blocks_args_str: list[str],
@@ -472,6 +474,7 @@ def _initialize_weights(self) -> None:
 
 
 class EfficientNetBN(EfficientNet):
+
     def __init__(
         self,
         model_name: str,
@@ -558,6 +561,7 @@ def __init__(
 
 
 class EfficientNetBNFeatures(EfficientNet):
+
     def __init__(
         self,
         model_name: str,

monai/networks/nets/highresnet.py

@@ -36,6 +36,7 @@
 
 
 class HighResBlock(nn.Module):
+
     def __init__(
         self,
         spatial_dims: int,