Update transforms in deepEdit App (#664)

Signed-off-by: Andres <[email protected]>

Co-authored-by: SACHIDANAND ALLE <[email protected]>

Author: diazandr3s

monailabel/deepedit/multilabel/transforms.py

@@ -635,44 +635,6 @@ def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.nda
         return d
 
 
-# A transform to get single modality if there are more and do label sanity
-class SingleModalityLabelSanityd(MapTransform):
-    """
-    Gets single modality and perform label sanity check
-
-    Error is the label is not in the same range:
-     https://stdworkflow.com/866/runtimeerror-cudnn-error-cudnn-status-not-initialized
-    """
-
-    def __init__(
-        self,
-        keys: KeysCollection,
-        label_names=None,
-        allow_missing_keys: bool = False,
-    ):
-        super().__init__(keys, allow_missing_keys)
-        self.label_names = label_names
-
-    def __call__(self, data):
-        d = dict(data)
-        for key in self.keys:
-            if key == "label":
-                logger.info(f"Input image shape check in SingleModalityLabelSanityd transform: {d[key].shape}")
-            if key == "image":
-                meta_data = d["image_meta_dict"]
-                if meta_data["spatial_shape"].shape[0] > 3:
-                    if meta_data["spatial_shape"][4] > 0:
-                        logger.info(
-                            f"Image {meta_data['filename_or_obj'].split('/')[-1]} has more than one modality "
-                            f"- taking FIRST modality ..."
-                        )
-
-                        d[key] = d[key][..., 0]
-                        meta_data["spatial_shape"][4] = 0.0
-
-        return d
-
-
 class AddGuidanceFromPointsCustomd(Transform):
     """
     Add guidance based on user clicks. ONLY WORKS FOR 3D

monailabel/deepedit/transforms.py

@@ -294,35 +294,6 @@ def __call__(self, data):
         return d
 
 
-# A transform to check for single modality and single label
-class CheckSingleLabelSingleModalityd(MapTransform):
-    """
-    Checks single modality and single label
-    """
-
-    def __call__(self, data):
-        d = dict(data)
-        for key in self.keys:
-            if key == "label":
-                meta_data = d["label_meta_dict"]
-                if len(np.unique(d[key])) > 2:
-                    logger.info(
-                        f"Label {meta_data['filename_or_obj'].split('/')[-1]} has more than one mask - "
-                        f" Consider using Multilabel DeepEdit App ... FINISHING"
-                    )
-                    raise Exception("Multilabel Error")
-
-            if key == "image":
-                meta_data = d["image_meta_dict"]
-                if d[key].shape[0] > 1:
-                    logger.error(
-                        f"Image {meta_data['filename_or_obj'].split('/')[-1]} has more than one modality "
-                        f"- Multimodality is not yet supported ... FINISHING"
-                    )
-                    raise Exception("Multimodality/Multisequence images NOT supported")
-        return d
-
-
 class CropGuidanceForegroundd(Transform):
     """
     Update guidance based on foreground crop.

monailabel/tasks/scoring/tta.py

@@ -30,7 +30,6 @@
     RandFlipd,
     RandRotated,
     Resized,
-    Spacingd,
     ToTensord,
 )
 from monai.transforms.compose import Compose
@@ -41,7 +40,7 @@
 from monai.utils.enums import CommonKeys, InverseKeys
 from tqdm import tqdm
 
-from monailabel.deepedit.transforms import CheckSingleLabelSingleModalityd, DiscardAddGuidanced
+from monailabel.deepedit.transforms import DiscardAddGuidanced
 from monailabel.interfaces.datastore import Datastore
 from monailabel.interfaces.tasks.scoring import ScoringMethod
 
@@ -73,9 +72,9 @@ def __init__(
     def pre_transforms(self):
         t = [
             LoadImaged(keys="image", reader="nibabelreader"),
-            CheckSingleLabelSingleModalityd(keys="image"),
             AddChanneld(keys="image"),
-            Spacingd(keys="image", pixdim=self.spacing),
+            # Spacing might not be needed as resize transform is used later.
+            # Spacingd(keys="image", pixdim=self.spacing),
             RandAffined(
                 keys="image",
                 prob=1,

sample-apps/deepedit/lib/infer.py

@@ -26,7 +26,7 @@
     ToTensord,
 )
 
-from monailabel.deepedit.transforms import CheckSingleLabelSingleModalityd, DiscardAddGuidanced, ResizeGuidanceCustomd
+from monailabel.deepedit.transforms import DiscardAddGuidanced, ResizeGuidanceCustomd
 from monailabel.interfaces.tasks.infer import InferTask, InferType
 from monailabel.transform.post import Restored
 
@@ -66,7 +66,6 @@ def pre_transforms(self, data=None) -> Sequence[Callable]:
         return [
             LoadImaged(keys="image"),
             EnsureChannelFirstd(keys="image"),
-            CheckSingleLabelSingleModalityd(keys="image"),
             Orientationd(keys="image", axcodes="RAS"),
             NormalizeIntensityd(keys="image"),
             Resized(keys="image", spatial_size=self.spatial_size, mode="area"),
@@ -122,7 +121,6 @@ def pre_transforms(self, data=None) -> Sequence[Callable]:
         return [
             LoadImaged(keys="image", reader="nibabelreader"),
             EnsureChannelFirstd(keys="image"),
-            CheckSingleLabelSingleModalityd(keys="image"),
             # Spacing might not be needed as resize transform is used later.
             # Spacingd(keys=["image", "label"], pixdim=self.target_spacing, mode=("bilinear", "nearest")),
             Orientationd(keys="image", axcodes="RAS"),

sample-apps/deepedit/lib/train.py

@@ -37,7 +37,7 @@
 
 from monailabel.deepedit.handlers import TensorBoardImageHandler
 from monailabel.deepedit.interaction import Interaction
-from monailabel.deepedit.transforms import CheckSingleLabelSingleModalityd, PosNegClickProbAddRandomGuidanced
+from monailabel.deepedit.transforms import PosNegClickProbAddRandomGuidanced
 from monailabel.tasks.train.basic_train import BasicTrainTask, Context
 
 logger = logging.getLogger(__name__)
@@ -94,7 +94,6 @@ def train_pre_transforms(self, context: Context):
         return [
             LoadImaged(keys=("image", "label"), reader="nibabelreader"),
             EnsureChannelFirstd(keys=("image", "label")),
-            CheckSingleLabelSingleModalityd(keys=("image", "label")),
             # Spacing might not be needed as resize transform is used later.
             # Spacingd(keys=["image", "label"], pixdim=self.target_spacing, mode=("bilinear", "nearest")),
             Orientationd(keys=["image", "label"], axcodes="RAS"),
@@ -127,7 +126,6 @@ def val_pre_transforms(self, context: Context):
         return [
             LoadImaged(keys=("image", "label"), reader="nibabelreader"),
             EnsureChannelFirstd(keys=("image", "label")),
-            CheckSingleLabelSingleModalityd(keys=("image", "label")),
             # Spacing might not be needed as resize transform is used later.
             # Spacingd(keys=["image", "label"], pixdim=self.target_spacing, mode=("bilinear", "nearest")),
             Orientationd(keys=["image", "label"], axcodes="RAS"),