fixed doc build; fixed isort

Signed-off-by: Lukas Folle <[email protected]>

Author: lukas-folle-snkeos

monai/transforms/__init__.py

@@ -506,7 +506,7 @@
     ZoomDict,
 )
 from .spatial.functional import spatial_resample
-from .traits import LazyTrait, MultiSampleTrait, RandomizableTrait, ThreadUnsafe
+from .traits import LazyTrait, MultiSampleTrait, RandomizableTrait, ReduceTrait, ThreadUnsafe
 from .transform import LazyTransform, MapTransform, Randomizable, RandomizableTransform, Transform, apply_transform
 from .utility.array import (
     AddCoordinateChannels,
@@ -521,6 +521,7 @@
     EnsureChannelFirst,
     EnsureType,
     FgBgToIndices,
+    FlattenSequence,
     Identity,
     ImageFilter,
     IntensityStats,
@@ -546,7 +547,6 @@
     TorchVision,
     ToTensor,
     Transpose,
-    FlattenSequence,
 )
 from .utility.dictionary import (
     AddCoordinateChannelsd,
@@ -594,6 +594,9 @@
     FgBgToIndicesd,
     FgBgToIndicesD,
     FgBgToIndicesDict,
+    FlattenSequenced,
+    FlattenSequenceD,
+    FlattenSequenceDict,
     FlattenSubKeysd,
     FlattenSubKeysD,
     FlattenSubKeysDict,
@@ -672,9 +675,6 @@
     Transposed,
     TransposeD,
     TransposeDict,
-    FlattenSequenced,
-    FlattenSequenceD,
-    FlattenSequenceDict,
 )
 from .utils import (
     Fourier,

monai/transforms/transform.py

@@ -25,7 +25,7 @@
 from monai import config, transforms
 from monai.config import KeysCollection
 from monai.data.meta_tensor import MetaTensor
-from monai.transforms.traits import LazyTrait, RandomizableTrait, ThreadUnsafe, ReduceTrait
+from monai.transforms.traits import LazyTrait, RandomizableTrait, ReduceTrait, ThreadUnsafe
 from monai.utils import MAX_SEED, ensure_tuple, first
 from monai.utils.enums import TransformBackends
 from monai.utils.misc import MONAIEnvVars

monai/transforms/utility/array.py

@@ -110,7 +110,7 @@
     "ImageFilter",
     "RandImageFilter",
     "ApplyTransformToPoints",
-    "FlattenSequence"
+    "FlattenSequence",
 ]
 
 

monai/transforms/utility/dictionary.py

@@ -45,6 +45,7 @@
     EnsureChannelFirst,
     EnsureType,
     FgBgToIndices,
+    FlattenSequence,
     Identity,
     ImageFilter,
     IntensityStats,
@@ -64,7 +65,6 @@
     TorchVision,
     ToTensor,
     Transpose,
-    FlattenSequence
 )
 from monai.transforms.utils import extreme_points_to_image, get_extreme_points
 from monai.transforms.utils_pytorch_numpy_unification import concatenate
@@ -194,7 +194,7 @@
     "ApplyTransformToPointsDict",
     "FlattenSequenced",
     "FlattenSequenceD",
-    "FlattenSequenceDict"
+    "FlattenSequenceDict",
 ]
 
 DEFAULT_POST_FIX = PostFix.meta()
@@ -1920,12 +1920,8 @@ class FlattenSequenced(MapTransform, ReduceTrait):
         allow_missing_keys:
             Don't raise exception if key is missing.
     """
-    def __init__(
-        self,
-        keys: KeysCollection,
-        allow_missing_keys: bool = False,
-        **kwargs,
-    ) -> None:
+
+    def __init__(self, keys: KeysCollection, allow_missing_keys: bool = False, **kwargs) -> None:
         super().__init__(keys, allow_missing_keys)
         self.flatten_sequence = FlattenSequence(**kwargs)
 

tests/transforms/compose/test_compose.py

@@ -290,14 +290,16 @@ def test_list_extend_multi_sample_trait(self):
         img = torch.zeros([1, 512, 512])
 
         self.assertEqual(execute_compose(img, [center_crop]).shape, torch.Size([1, 128, 128]))
-        single_multi_sample_trait_result = execute_compose(img, [multi_sample_transform, center_crop, flatten_sequence_transform])
+        single_multi_sample_trait_result = execute_compose(
+            img, [multi_sample_transform, center_crop, flatten_sequence_transform]
+        )
         self.assertIsInstance(single_multi_sample_trait_result, list)
         self.assertEqual(len(single_multi_sample_trait_result), 1)
         self.assertEqual(single_multi_sample_trait_result[0].shape, torch.Size([1, 64, 64]))
 
-        double_multi_sample_trait_result = execute_compose(img, [
-            multi_sample_transform, multi_sample_transform, flatten_sequence_transform, center_crop
-        ])
+        double_multi_sample_trait_result = execute_compose(
+            img, [multi_sample_transform, multi_sample_transform, flatten_sequence_transform, center_crop]
+        )
         self.assertIsInstance(double_multi_sample_trait_result, list)
         self.assertEqual(len(double_multi_sample_trait_result), 1)
         self.assertEqual(double_multi_sample_trait_result[0].shape, torch.Size([1, 64, 64]))