Independent preprocessor for generating validation plots

src/batdetect2/train/callbacks.py

@@ -9,6 +9,7 @@ from batdetect2.evaluate.match import (
     MatchConfig,
     match_all_predictions,
 )
+from batdetect2.plotting.clips import PreprocessorProtocol
 from batdetect2.plotting.evaluation import plot_example_gallery
 from batdetect2.postprocess import get_raw_predictions
 from batdetect2.train.dataset import ValidationDataset
@@ -27,6 +28,7 @@ class ValidationMetrics(Callback):
     def __init__(
         self,
         metrics: List[MetricsProtocol],
+        preprocessor: PreprocessorProtocol,
         plot: bool = True,
         match_config: Optional[MatchConfig] = None,
     ):
@@ -37,6 +39,7 @@ class ValidationMetrics(Callback):
 
         self.match_config = match_config
         self.metrics = metrics
+        self.preprocessor = preprocessor
         self.plot = plot
 
         self._clip_annotations: List[data.ClipAnnotation] = []
@@ -61,7 +64,7 @@ class ValidationMetrics(Callback):
 
         for class_name, fig in plot_example_gallery(
             matches,
-            preprocessor=pl_module.model.preprocessor,
+            preprocessor=self.preprocessor,
             n_examples=4,
         ):
             plotter(

src/batdetect2/train/train.py

@@ -126,7 +126,9 @@ def build_training_module(
 
 
 def build_trainer_callbacks(
-    targets: TargetProtocol, config: EvaluationConfig
+    targets: TargetProtocol,
+    preprocessor: PreprocessorProtocol,
+    config: EvaluationConfig,
 ) -> List[Callback]:
     return [
         ModelCheckpoint(
@@ -142,6 +144,7 @@ def build_trainer_callbacks(
                 ),
                 ClassificationAccuracy(class_names=targets.class_names),
             ],
+            preprocessor=preprocessor,
             match_config=config.match,
         ),
     ]
@@ -163,7 +166,11 @@ def build_trainer(
     return Trainer(
         **trainer_conf.model_dump(exclude_none=True),
         logger=train_logger,
-        callbacks=build_trainer_callbacks(targets, config=conf.evaluation),
+        callbacks=build_trainer_callbacks(
+            targets,
+            config=conf.evaluation,
+            preprocessor=build_preprocessor(conf.preprocess),
+        ),
     )