Fix UNETR Model for LIVECell

experiments/vision-transformer/unetr/livecell/common.py

@@ -93,16 +93,18 @@ def get_unetr_model(
         source_choice: str,
         patch_shape: Tuple[int, int],
         sam_initialization: bool,
-        output_channels: int
+        output_channels: int,
+        backbone: str = "sam"
 ):
     """Returns the expected UNETR model
     """
     if source_choice == "torch-em":
         # this returns the unetr model whihc uses the vision transformer from segment anything
         from torch_em import model as torch_em_models
         model = torch_em_models.UNETR(
-            encoder=model_name, out_channels=output_channels,
-            encoder_checkpoint_path=MODELS[model_name] if sam_initialization else None
+            backbone=backbone, encoder=model_name, out_channels=output_channels,
+            encoder_checkpoint_path=MODELS[model_name] if sam_initialization else None,
+            use_sam_stats=sam_initialization  # FIXME: add mae weight initialization
         )
 
     elif source_choice == "monai":
@@ -117,7 +119,9 @@ def get_unetr_model(
         model.out_channels = 2  # type: ignore
 
     else:
-        raise ValueError(f"The available UNETR models are either from \"torch-em\" or \"monai\", choose from them instead of - {source_choice}")
+        tmp_msg = "The available UNETR models are either from `torch-em` or `monai`. "
+        tmp_msg += f"Please choose from them instead of {source_choice}"
+        raise ValueError(tmp_msg)
 
     return model
 

experiments/vision-transformer/unetr/livecell/livecell_all_unetr.py

@@ -126,7 +126,8 @@ def main(args):
     # get the model for the training and inference on livecell dataset
     model = common.get_unetr_model(
         model_name=args.model_name, source_choice=args.source_choice, patch_shape=patch_shape,
-        sam_initialization=args.do_sam_ini, output_channels=common._get_output_channels(args.with_affinities)
+        sam_initialization=args.do_sam_ini, output_channels=common._get_output_channels(args.with_affinities),
+        backbone=args.pretrained_choice
     )
     model.to(device)
 
@@ -137,7 +138,7 @@ def main(args):
         print("2d UNETR training on LIVECell dataset")
         # get the desired livecell loaders for training
         train_loader, val_loader = common.get_my_livecell_loaders(
-            args.input, patch_shape, args.cell_type,
+            args.input, patch_shape, args.cell_type, with_boundary=not args.with_affinities,
             with_affinities=args.with_affinities  # this takes care of getting the loaders with affinities
         )
         do_unetr_training(