Updated callback to include plotting

src/batdetect2/train/callbacks.py

@@ -2,11 +2,13 @@ from typing import List
 
 from lightning import LightningModule, Trainer
 from lightning.pytorch.callbacks import Callback
+from lightning.pytorch.loggers import TensorBoardLogger
 from soundevent import data
 from torch.utils.data import DataLoader
 
 from batdetect2.evaluate.match import match_sound_events_and_raw_predictions
-from batdetect2.evaluate.types import Match, MetricsProtocol
+from batdetect2.evaluate.types import MatchEvaluation, MetricsProtocol
+from batdetect2.plotting.evaluation import plot_examples
 from batdetect2.targets.types import TargetProtocol
 from batdetect2.train.dataset import LabeledDataset, TrainExample
 from batdetect2.train.lightning import TrainingModule
@@ -14,25 +16,55 @@ from batdetect2.train.types import ModelOutput
 
 
 class ValidationMetrics(Callback):
-    def __init__(self, metrics: List[MetricsProtocol]):
+    def __init__(self, metrics: List[MetricsProtocol], plot: bool = True):
         super().__init__()
 
         if len(metrics) == 0:
             raise ValueError("At least one metric needs to be provided")
 
-        self.matches: List[Match] = []
+        self.matches: List[MatchEvaluation] = []
         self.metrics = metrics
+        self.plot = plot
+
+    def get_dataset(self, trainer: Trainer) -> LabeledDataset:
+        dataloaders = trainer.val_dataloaders
+        assert isinstance(dataloaders, DataLoader)
+        dataset = dataloaders.dataset
+        assert isinstance(dataset, LabeledDataset)
+        return dataset
+
+    def plot_examples(self, pl_module: LightningModule):
+        if not isinstance(pl_module.logger, TensorBoardLogger):
+            return
+
+        for class_name, fig in plot_examples(
+            self.matches,
+            preprocessor=pl_module.preprocessor,
+            n_examples=5,
+        ):
+            pl_module.logger.experiment.add_figure(
+                f"{class_name}/examples",
+                fig,
+                pl_module.global_step,
+            )
+
+    def log_metrics(self, pl_module: LightningModule):
+        metrics = {}
+        for metric in self.metrics:
+            metrics.update(metric(self.matches).items())
+
+        pl_module.log_dict(metrics)
 
     def on_validation_epoch_end(
         self,
         trainer: Trainer,
         pl_module: LightningModule,
     ) -> None:
-        metrics = {}
-        for metric in self.metrics:
-            metrics.update(metric(self.matches).items())
+        self.log_metrics(pl_module)
+
+        if self.plot:
+            self.plot_examples(pl_module)
 
-        pl_module.log_dict(metrics)
         return super().on_validation_epoch_end(trainer, pl_module)
 
     def on_validation_epoch_start(
@@ -52,11 +84,7 @@ class ValidationMetrics(Callback):
         batch_idx: int,
         dataloader_idx: int = 0,
     ) -> None:
-        dataloaders = trainer.val_dataloaders
-        assert isinstance(dataloaders, DataLoader)
-
-        dataset = dataloaders.dataset
-        assert isinstance(dataset, LabeledDataset)
+        dataset = self.get_dataset(trainer)
 
         clip_annotations = [
             _get_subclip(
@@ -74,7 +102,7 @@ class ValidationMetrics(Callback):
 
         clips = [clip_annotation.clip for clip_annotation in clip_annotations]
 
-        raw_predictions = pl_module.postprocessor.get_raw_predictions(
+        raw_predictions = pl_module.postprocessor.get_sound_event_predictions(
             outputs,
             clips,
         )
@@ -84,7 +112,7 @@ class ValidationMetrics(Callback):
         ):
             self.matches.extend(
                 match_sound_events_and_raw_predictions(
-                    sound_events=clip_annotation.sound_events,
+                    clip_annotation=clip_annotation,
                     raw_predictions=clip_predictions,
                     targets=pl_module.targets,
                 )

src/batdetect2/train/lightning.py

@@ -48,20 +48,19 @@ class TrainingModule(L.LightningModule):
     def training_step(self, batch: TrainExample):
         outputs = self.forward(batch.spec)
         losses = self.loss(outputs, batch)
-
         self.log("total_loss/train", losses.total, prog_bar=True, logger=True)
         self.log("detection_loss/train", losses.total, logger=True)
         self.log("size_loss/train", losses.total, logger=True)
         self.log("classification_loss/train", losses.total, logger=True)
-
         return losses.total
 
     def validation_step(  # type: ignore
-        self, batch: TrainExample, batch_idx: int
+        self,
+        batch: TrainExample,
+        batch_idx: int,
     ) -> ModelOutput:
         outputs = self.forward(batch.spec)
         losses = self.loss(outputs, batch)
-
         self.log("total_loss/val", losses.total, prog_bar=True, logger=True)
         self.log("detection_loss/val", losses.total, logger=True)
         self.log("size_loss/val", losses.total, logger=True)