Fix Invertd

monai/apps/detection/transforms/box_ops.py

@@ -267,7 +267,7 @@ def convert_box_to_mask(
             boxes_only_mask = np.ones(box_size, dtype=np.int16) * np.int16(labels_np[b])
         # apply to global mask
         slicing = [b]
-        slicing.extend(slice(boxes_np[b, d], boxes_np[b, d + spatial_dims]) for d in range(spatial_dims))  # type:ignore
+        slicing.extend(slice(boxes_np[b, d], boxes_np[b, d + spatial_dims]) for d in range(spatial_dims))  # type: ignore
         boxes_mask_np[tuple(slicing)] = boxes_only_mask
     return convert_to_dst_type(src=boxes_mask_np, dst=boxes, dtype=torch.int16)[0]
 

monai/auto3dseg/analyzer.py

@@ -105,7 +105,7 @@ def update_ops_nested_label(self, nested_key: str, op: Operations) -> None:
             raise ValueError("Nested_key input format is wrong. Please ensure it is like key1#0#key2")
         root: str
         child_key: str
-        (root, _, child_key) = keys
+        root, _, child_key = keys
         if root not in self.ops:
             self.ops[root] = [{}]
         self.ops[root][0].update({child_key: None})

monai/bundle/scripts.py

@@ -1948,7 +1948,7 @@ def create_workflow(
 
     """
     _args = update_kwargs(args=args_file, workflow_name=workflow_name, config_file=config_file, **kwargs)
-    (workflow_name, config_file) = _pop_args(
+    workflow_name, config_file = _pop_args(
         _args, workflow_name=ConfigWorkflow, config_file=None
     )  # the default workflow name is "ConfigWorkflow"
     if isinstance(workflow_name, str):

monai/data/dataset.py

@@ -139,7 +139,7 @@ class DatasetFunc(Dataset):
     """
 
     def __init__(self, data: Any, func: Callable, **kwargs) -> None:
-        super().__init__(data=None, transform=None)  # type:ignore
+        super().__init__(data=None, transform=None)  # type: ignore
         self.src = data
         self.func = func
         self.kwargs = kwargs
@@ -1635,7 +1635,7 @@ def _cachecheck(self, item_transformed):
                         return (_data, _meta)
                     return _data
                 else:
-                    item: list[dict[Any, Any]] = [{} for _ in range(len(item_transformed))]  # type:ignore
+                    item: list[dict[Any, Any]] = [{} for _ in range(len(item_transformed))]  # type: ignore
                     for i, _item in enumerate(item_transformed):
                         for k in _item:
                             meta_i_k = self._load_meta_cache(meta_hash_file_name=f"{hashfile.name}-{k}-meta-{i}")

monai/handlers/utils.py

@@ -48,7 +48,7 @@ def stopping_fn_from_loss() -> Callable[[Engine], Any]:
     """
 
     def stopping_fn(engine: Engine) -> Any:
-        return -engine.state.output  # type:ignore
+        return -engine.state.output  # type: ignore
 
     return stopping_fn
 

monai/metrics/utils.py

@@ -320,7 +320,7 @@ def get_edge_surface_distance(
     edges_spacing = None
     if use_subvoxels:
         edges_spacing = spacing if spacing is not None else ([1] * len(y_pred.shape))
-    (edges_pred, edges_gt, *areas) = get_mask_edges(
+    edges_pred, edges_gt, *areas = get_mask_edges(
         y_pred, y, crop=True, spacing=edges_spacing, always_return_as_numpy=False
     )
     if not edges_gt.any():

monai/transforms/inverse.py

@@ -119,6 +119,8 @@ def get_transform_info(self) -> dict:
         """
         Return a dictionary with the relevant information pertaining to an applied transform.
         """
+        self._init_trace_threadlocal()
+
         vals = (
             self.__class__.__name__,
             id(self),
@@ -300,25 +302,45 @@ def track_transform_meta(
         return out_obj
 
     def check_transforms_match(self, transform: Mapping) -> None:
-        """Check transforms are of same instance."""
-        xform_id = transform.get(TraceKeys.ID, "")
-        if xform_id == id(self):
-            return
-        # TraceKeys.NONE to skip the id check
-        if xform_id == TraceKeys.NONE:
+        """Check whether a traced transform entry matches this transform.
+
+        When multiprocessing uses ``spawn``, transform instances are recreated,
+        so matching can fall back to the transform class name instead of the
+        original instance ID.
+        """
+        if self._transforms_match(transform):
             return
+
+        xform_id = transform.get(TraceKeys.ID, "")
         xform_name = transform.get(TraceKeys.CLASS_NAME, "")
         warning_msg = transform.get(TraceKeys.EXTRA_INFO, {}).get("warn")
         if warning_msg:
             warnings.warn(warning_msg)
-        # basic check if multiprocessing uses 'spawn' (objects get recreated so don't have same ID)
-        if torch.multiprocessing.get_start_method() in ("spawn", None) and xform_name == self.__class__.__name__:
-            return
         raise RuntimeError(
             f"Error {self.__class__.__name__} getting the most recently "
             f"applied invertible transform {xform_name} {xform_id} != {id(self)}."
         )
 
+    def _transforms_match(self, transform: Mapping) -> bool:
+        """Return whether a traced transform entry matches this transform.
+
+        Matching succeeds when the traced ID matches this instance, when the ID
+        check is explicitly disabled with ``TraceKeys.NONE``, or when
+        multiprocessing uses ``spawn`` and the traced class name matches this
+        transform class.
+        """
+        xform_id = transform.get(TraceKeys.ID, "")
+        if xform_id == id(self):
+            return True
+        # TraceKeys.NONE to skip the id check
+        if xform_id == TraceKeys.NONE:
+            return True
+        xform_name = transform.get(TraceKeys.CLASS_NAME, "")
+        # basic check if multiprocessing uses 'spawn' (objects get recreated so don't have same ID)
+        if torch.multiprocessing.get_start_method(allow_none=True) == "spawn" and xform_name == self.__class__.__name__:
+            return True
+        return False
+
     def get_most_recent_transform(self, data, key: Hashable = None, check: bool = True, pop: bool = False):
         """
         Get most recent matching transform for the current class from the sequence of applied operations.
@@ -350,10 +372,16 @@ def get_most_recent_transform(self, data, key: Hashable = None, check: bool = Tr
         if not all_transforms:
             raise ValueError(f"Item of type {type(data)} (key: {key}, pop: {pop}) has empty 'applied_operations'")
 
+        match_idx = len(all_transforms) - 1
         if check:
-            self.check_transforms_match(all_transforms[-1])
+            for idx in range(len(all_transforms) - 1, -1, -1):
+                if self._transforms_match(all_transforms[idx]):
+                    match_idx = idx
+                    break
+            else:
+                self.check_transforms_match(all_transforms[-1])
 
-        return all_transforms.pop(-1) if pop else all_transforms[-1]
+        return all_transforms.pop(match_idx) if pop else all_transforms[match_idx]
 
     def pop_transform(self, data, key: Hashable = None, check: bool = True):
         """

monai/transforms/io/array.py

@@ -11,6 +11,7 @@
 """
 A collection of "vanilla" transforms for IO functions.
 """
+
 from __future__ import annotations
 
 import inspect

monai/transforms/utility/array.py

@@ -702,7 +702,7 @@ def __init__(
                 # if the root log level is higher than INFO, set a separate stream handler to record
                 console = logging.StreamHandler(sys.stdout)
                 console.setLevel(logging.INFO)
-                console.is_data_stats_handler = True  # type:ignore[attr-defined]
+                console.is_data_stats_handler = True  # type: ignore[attr-defined]
                 _logger.addHandler(console)
 
     def __call__(

tests/integration/test_loader_semaphore.py

@@ -10,6 +10,7 @@
 # limitations under the License.
 """this test should not generate errors or
 UserWarning: semaphore_tracker: There appear to be 1 leaked semaphores"""
+
 from __future__ import annotations
 
 import multiprocessing as mp

tests/profile_subclass/profiling.py

@@ -12,6 +12,7 @@
 Comparing torch.Tensor, SubTensor, SubWithTorchFunc, MetaTensor
 Adapted from https://github.com/pytorch/pytorch/tree/v1.11.0/benchmarks/overrides_benchmark
 """
+
 from __future__ import annotations
 
 import argparse

tests/profile_subclass/pyspy_profiling.py

@@ -12,6 +12,7 @@
 To be used with py-spy, comparing torch.Tensor, SubTensor, SubWithTorchFunc, MetaTensor
 Adapted from https://github.com/pytorch/pytorch/tree/v1.11.0/benchmarks/overrides_benchmark
 """
+
 from __future__ import annotations
 
 import argparse

tests/transforms/croppad/test_pad_nd_dtypes.py

@@ -12,6 +12,7 @@
 Tests for pad_nd dtype support and backend selection.
 Validates PyTorch padding preference and NumPy fallback behavior.
 """
+
 from __future__ import annotations
 
 import unittest