Merge branch 'Project-MONAI:dev' into dev

docs/source/engines.rst

@@ -30,6 +30,11 @@ Workflows
 .. autoclass:: GanTrainer
     :members:
 
+`AdversarialTrainer`
+~~~~~~~~~~~~~~~~~~~~
+.. autoclass:: AdversarialTrainer
+    :members:
+
 `Evaluator`
 ~~~~~~~~~~~
 .. autoclass:: Evaluator

docs/source/inferers.rst

@@ -49,6 +49,29 @@ Inferers
     :members:
     :special-members: __call__
 
+`DiffusionInferer`
+~~~~~~~~~~~~~~~~~~
+.. autoclass:: DiffusionInferer
+    :members:
+    :special-members: __call__
+
+`LatentDiffusionInferer`
+~~~~~~~~~~~~~~~~~~~~~~~~
+.. autoclass:: LatentDiffusionInferer
+    :members:
+    :special-members: __call__
+
+`ControlNetDiffusionInferer`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. autoclass:: ControlNetDiffusionInferer
+    :members:
+    :special-members: __call__
+
+`ControlNetLatentDiffusionInferer`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. autoclass:: ControlNetLatentDiffusionInferer
+    :members:
+    :special-members: __call__
 
 Splitters
 ---------

docs/source/utils.rst

@@ -81,3 +81,8 @@ Component store
 ---------------
 .. autoclass:: monai.utils.component_store.ComponentStore
   :members:
+
+Ordering
+--------
+.. automodule:: monai.utils.ordering
+  :members:

monai/apps/auto3dseg/hpo_gen.py

@@ -53,7 +53,7 @@ def update_params(self, *args, **kwargs):
         raise NotImplementedError
 
     @abstractmethod
-    def set_score(self):
+    def set_score(self, *args, **kwargs):
         """Report the evaluated results to HPO."""
         raise NotImplementedError
 

monai/apps/detection/utils/anchor_utils.py

@@ -189,7 +189,7 @@ def generate_anchors(
             w_ratios = 1 / area_scale
             h_ratios = area_scale
         # if 3d, w:h:d = 1:aspect_ratios[:,0]:aspect_ratios[:,1]
-        elif self.spatial_dims == 3:
+        else:
             area_scale = torch.pow(aspect_ratios_t[:, 0] * aspect_ratios_t[:, 1], 1 / 3.0)
             w_ratios = 1 / area_scale
             h_ratios = aspect_ratios_t[:, 0] / area_scale
@@ -199,7 +199,7 @@ def generate_anchors(
         hs = (h_ratios[:, None] * scales_t[None, :]).view(-1)
         if self.spatial_dims == 2:
             base_anchors = torch.stack([-ws, -hs, ws, hs], dim=1) / 2.0
-        elif self.spatial_dims == 3:
+        else:  # elif self.spatial_dims == 3:
             ds = (d_ratios[:, None] * scales_t[None, :]).view(-1)
             base_anchors = torch.stack([-ws, -hs, -ds, ws, hs, ds], dim=1) / 2.0
 

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

@@ -28,7 +28,7 @@
     SobelGradients,
 )
 from monai.transforms.transform import Transform
-from monai.transforms.utils_pytorch_numpy_unification import max, maximum, min, sum, unique
+from monai.transforms.utils_pytorch_numpy_unification import max, maximum, min, sum, unique, where
 from monai.utils import TransformBackends, convert_to_numpy, optional_import
 from monai.utils.misc import ensure_tuple_rep
 from monai.utils.type_conversion import convert_to_dst_type, convert_to_tensor
@@ -162,7 +162,8 @@ 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
+        pred_indices = np.where(pred > 0)
+        pred[pred_indices] = 1
 
         return convert_to_dst_type(pred, prob_map, dtype=self.dtype)[0]
 
@@ -338,7 +339,8 @@ 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
+        marker_indices = where(marker < 0)
+        marker[marker_indices] = 0  # type: ignore[index]
         marker = self.postprocess_fn(marker)
         marker = convert_to_numpy(marker)
 
@@ -379,6 +381,7 @@ def _generate_contour_coord(self, current: np.ndarray, previous: np.ndarray) ->
         """
 
         p_delta = (current[0] - previous[0], current[1] - previous[1])
+        row, col = -1, -1
 
         if p_delta in ((0.0, 1.0), (0.5, 0.5), (1.0, 0.0)):
             row = int(current[0] + 0.5)
@@ -634,7 +637,7 @@ 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]
+        inst_type = type_map_crop[seg_map_crop]  # type: ignore[index]
         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/bundle/utils.py

@@ -221,6 +221,7 @@ def load_bundle_config(bundle_path: str, *config_names: str, **load_kw_args: Any
                 raise ValueError(f"Cannot find config file '{full_cname}'")
 
             ardata = archive.read(full_cname)
+            cdata = {}
 
             if full_cname.lower().endswith("json"):
                 cdata = json.loads(ardata, **load_kw_args)

monai/data/dataset_summary.py

@@ -84,6 +84,7 @@ def collect_meta_data(self):
         """
 
         for data in self.data_loader:
+            meta_dict = {}
             if isinstance(data[self.image_key], MetaTensor):
                 meta_dict = data[self.image_key].meta
             elif self.meta_key in data:

monai/data/meta_tensor.py

@@ -505,7 +505,7 @@ def peek_pending_rank(self):
         a = self.pending_operations[-1].get(LazyAttr.AFFINE, None) if self.pending_operations else self.affine
         return 1 if a is None else int(max(1, len(a) - 1))
 
-    def new_empty(self, size, dtype=None, device=None, requires_grad=False):
+    def new_empty(self, size, dtype=None, device=None, requires_grad=False):  # type: ignore[override]
         """
         must be defined for deepcopy to work
 
@@ -580,7 +580,7 @@ def ensure_torch_and_prune_meta(
             img.affine = MetaTensor.get_default_affine()
         return img
 
-    def __repr__(self):
+    def __repr__(self):  # type: ignore[override]
         """
         Prints a representation of the tensor.
         Prepends "meta" to ``torch.Tensor.__repr__``.

monai/data/utils.py

@@ -53,10 +53,6 @@
     pytorch_after,
 )
 
-if pytorch_after(1, 13):
-    # import private code for reuse purposes, comment in case things break in the future
-    from torch.utils.data._utils.collate import collate_tensor_fn, default_collate_fn_map
-
 pd, _ = optional_import("pandas")
 DataFrame, _ = optional_import("pandas", name="DataFrame")
 nib, _ = optional_import("nibabel")
@@ -454,8 +450,13 @@ def collate_meta_tensor_fn(batch, *, collate_fn_map=None):
     Collate a sequence of meta tensor into a single batched metatensor. This is called by `collage_meta_tensor`
     and so should not be used as a collate function directly in dataloaders.
     """
-    collate_fn = collate_tensor_fn if pytorch_after(1, 13) else default_collate
-    collated = collate_fn(batch)  # type: ignore
+    if pytorch_after(1, 13):
+        from torch.utils.data._utils.collate import collate_tensor_fn  # imported here for pylint/mypy issues
+
+        collated = collate_tensor_fn(batch)
+    else:
+        collated = default_collate(batch)
+
     meta_dicts = [i.meta or TraceKeys.NONE for i in batch]
     common_ = set.intersection(*[set(d.keys()) for d in meta_dicts if isinstance(d, dict)])
     if common_:
@@ -496,6 +497,8 @@ def list_data_collate(batch: Sequence):
 
     if pytorch_after(1, 13):
         # needs to go here to avoid circular import
+        from torch.utils.data._utils.collate import default_collate_fn_map
+
         from monai.data.meta_tensor import MetaTensor
 
         default_collate_fn_map.update({MetaTensor: collate_meta_tensor_fn})

monai/engines/__init__.py

@@ -12,12 +12,14 @@
 from __future__ import annotations
 
 from .evaluator import EnsembleEvaluator, Evaluator, SupervisedEvaluator
-from .trainer import GanTrainer, SupervisedTrainer, Trainer
+from .trainer import AdversarialTrainer, GanTrainer, SupervisedTrainer, Trainer
 from .utils import (
+    DiffusionPrepareBatch,
     IterationEvents,
     PrepareBatch,
     PrepareBatchDefault,
     PrepareBatchExtraInput,
+    VPredictionPrepareBatch,
     default_make_latent,
     default_metric_cmp_fn,
     default_prepare_batch,