Delete unused lines - run from main function (#7)

- Remove unused functionalities
- Run from the main function

---------

Signed-off-by: Andres <diazandr3s@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: diazandr3s

monailabel/monaivista/lib/configs/vista_point_2pt5.py

@@ -16,16 +16,9 @@
 import lib.infers
 import lib.trainers
 from lib.model.vista_point_2pt5.models_samm2pt5d import sam_model_registry
-from monai.networks.nets import UNet
 from monailabel.interfaces.config import TaskConfig
 from monailabel.interfaces.tasks.infer_v2 import InferTask
-from monailabel.interfaces.tasks.scoring import ScoringMethod
-from monailabel.interfaces.tasks.strategy import Strategy
 from monailabel.interfaces.tasks.train import TrainTask
-from monailabel.tasks.activelearning.epistemic import Epistemic
-from monailabel.tasks.scoring.dice import Dice
-from monailabel.tasks.scoring.epistemic import EpistemicScoring
-from monailabel.tasks.scoring.sum import Sum
 from monailabel.utils.others.generic import download_file, strtobool
 
 logger = logging.getLogger(__name__)
@@ -35,9 +28,6 @@ class VISTAPOINT2PT5(TaskConfig):
     def __init__(self):
         super().__init__()
 
-        self.epistemic_enabled = None
-        self.epistemic_samples = None
-
     def init(self, name: str, model_dir: str, conf: Dict[str, str], planner: Any, **kwargs):
         super().init(name, model_dir, conf, planner, **kwargs)
 
@@ -166,10 +156,6 @@ def init(self, name: str, model_dir: str, conf: Dict[str, str], planner: Any, **
         self.roi_size = (96, 96, 96)
 
         self.network = sam_model_registry["vit_b"](checkpoint=None, image_size=1024, encoder_in_chans=9 * 3)
-        # Others
-        self.epistemic_enabled = strtobool(conf.get("epistemic_enabled", "false"))
-        self.epistemic_samples = int(conf.get("epistemic_samples", "5"))
-        logger.info(f"EPISTEMIC Enabled: {self.epistemic_enabled}; Samples: {self.epistemic_samples}")
 
     def infer(self) -> Union[InferTask, Dict[str, InferTask]]:
         task: InferTask = lib.infers.VISTAPOINT2PT5(
@@ -179,50 +165,3 @@ def infer(self) -> Union[InferTask, Dict[str, InferTask]]:
             preload=strtobool(self.conf.get("preload", "false")),
         )
         return task
-
-    def trainer(self) -> Optional[TrainTask]:
-        output_dir = os.path.join(self.model_dir, self.name)
-        load_path = self.path[0] if os.path.exists(self.path[0]) else self.path[1]
-
-        task: TrainTask = lib.trainers.SegmentationSpleen(
-            model_dir=output_dir,
-            network=self.network,
-            roi_size=self.roi_size,
-            target_spacing=self.target_spacing,
-            description="Train Spleen Segmentation Model",
-            load_path=load_path,
-            publish_path=self.path[1],
-            labels=self.labels,
-            disable_meta_tracking=False,
-        )
-        return task
-
-    def strategy(self) -> Union[None, Strategy, Dict[str, Strategy]]:
-        strategies: Dict[str, Strategy] = {}
-        if self.epistemic_enabled:
-            strategies[f"{self.name}_epistemic"] = Epistemic()
-        return strategies
-
-    def scoring_method(self) -> Union[None, ScoringMethod, Dict[str, ScoringMethod]]:
-        methods: Dict[str, ScoringMethod] = {
-            "dice": Dice(),
-            "sum": Sum(),
-        }
-
-        if self.epistemic_enabled:
-            methods[f"{self.name}_epistemic"] = EpistemicScoring(
-                model=self.path,
-                network=UNet(
-                    spatial_dims=3,
-                    in_channels=1,
-                    out_channels=2,
-                    channels=[16, 32, 64, 128, 256],
-                    strides=[2, 2, 2, 2],
-                    num_res_units=2,
-                    norm="batch",
-                    dropout=0.2,
-                ),
-                transforms=lib.infers.SegmentationSpleen(None).pre_transforms(),
-                num_samples=self.epistemic_samples,
-            )
-        return methods

monailabel/monaivista/lib/infers/vista_point_2pt5.py

@@ -27,7 +27,7 @@
 
 class VISTAPOINT2PT5(BasicInferTask):
     """
-    This provides Inference Engine for pre-trained spleen segmentation (UNet) model over MSD Dataset.
+    This provides Inference Engine for pre-trained VISTA segmentation model.
     """
 
     def __init__(
@@ -52,6 +52,9 @@ def __init__(
         )
         self.target_spacing = target_spacing
 
+    def is_valid(self) -> bool:
+        return True
+
     def pre_transforms(self, data=None) -> Sequence[Callable]:
         return [
             LoadImaged(keys="image"),

monailabel/monaivista/lib/trainers/__init__.py

@@ -8,7 +8,3 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
-
-from .segmentation import Segmentation
-from .segmentation_spleen import SegmentationSpleen