Update example config

example_data/config.yaml

@@ -138,36 +138,49 @@ train:
     name: csv
 
   validation:
-    metrics:
+    tasks:
-      - name: detection_ap
+      - name: sound_event_detection
-      - name: classification_ap
+        metrics:
-    plots:
+          - name: average_precision
-      - name: example_gallery
+      - name: sound_event_classification
-      - name: example_clip
+        metrics:
-      - name: detection_pr_curve
+          - name: average_precision
-      - name: classification_pr_curves
-      - name: detection_roc_curve
-      - name: classification_roc_curves
 
 evaluation:
-  match_strategy:
+  tasks:
-    name: start_time_match
+    - name: sound_event_detection
-    distance_threshold: 0.01
+      metrics:
-  metrics:
+        - name: average_precision
-    - name: classification_ap
+        - name: roc_auc
-    - name: detection_ap
+      plots:
-    - name: detection_roc_auc
+        - name: pr_curve
-    - name: classification_roc_auc
+        - name: score_distribution
-    - name: top_class_ap
+        - name: example_detection
-    - name: classification_balanced_accuracy
+    - name: sound_event_classification
-    - name: clip_multiclass_ap
+      metrics:
-    - name: clip_multiclass_roc_auc
+        - name: average_precision
-    - name: clip_detection_ap
+        - name: roc_auc
-    - name: clip_detection_roc_auc
+      plots:
-  plots:
+        - name: pr_curve
-    - name: example_gallery
+    - name: top_class_detection
-    - name: example_clip
+      metrics:
-    - name: detection_pr_curve
+        - name: average_precision
-    - name: classification_pr_curves
+      plots:
-    - name: detection_roc_curve
+        - name: pr_curve
-    - name: classification_roc_curves
+        - name: confusion_matrix
+        - name: example_classification
+    - name: clip_detection
+      metrics:
+        - name: average_precision
+        - name: roc_auc
+      plots:
+        - name: pr_curve
+        - name: roc_curve
+        - name: score_distribution
+    - name: clip_classification
+      metrics:
+        - name: average_precision
+        - name: roc_auc
+      plots:
+        - name: pr_curve
+        - name: roc_curve

src/batdetect2/evaluate/lightning.py

@@ -4,8 +4,7 @@ from lightning import LightningModule
 from torch.utils.data import DataLoader
 
 from batdetect2.evaluate.dataset import TestDataset, TestExample
-from batdetect2.evaluate.tables import FullEvaluationTable
+from batdetect2.logging import get_image_logger
-from batdetect2.logging import get_image_logger, get_table_logger
 from batdetect2.models import Model
 from batdetect2.postprocess import to_raw_predictions
 from batdetect2.typing import ClipMatches, EvaluatorProtocol
@@ -54,16 +53,6 @@ class EvaluationModule(LightningModule):
     def on_test_epoch_end(self):
         self.log_metrics(self.clip_evaluations)
         self.plot_examples(self.clip_evaluations)
-        self.log_table(self.clip_evaluations)
-
-    def log_table(self, evaluated_clips: Sequence[ClipMatches]):
-        table_logger = get_table_logger(self.logger)  # type: ignore
-
-        if table_logger is None:
-            return
-
-        df = FullEvaluationTable()(evaluated_clips)
-        table_logger("full_evaluation", df, 0)
 
     def plot_examples(self, evaluated_clips: Sequence[ClipMatches]):
         plotter = get_image_logger(self.logger)  # type: ignore

src/batdetect2/train/augmentations.py

@@ -105,6 +105,14 @@ class MixAudio(torch.nn.Module):
         samplerate: int,
         source: Optional[AudioSource],
     ):
+        if source is None:
+            warnings.warn(
+                "Mix audio augmentation ('mix_audio') requires an "
+                "'example_source' callable to be provided.",
+                stacklevel=2,
+            )
+            return lambda wav, clip_annotation: (wav, clip_annotation)
+
         return MixAudio(
             example_source=source,
             min_weight=config.min_weight,
@@ -197,7 +205,9 @@ class AddEcho(torch.nn.Module):
     @audio_augmentations.register(AddEchoConfig)
     @staticmethod
     def from_config(
-        config: AddEchoConfig, samplerate: int, source: AudioSource
+        config: AddEchoConfig,
+        samplerate: int,
+        source: Optional[AudioSource],
     ):
         return AddEcho(
             samplerate=samplerate,
@@ -662,6 +672,30 @@ def build_audio_augmentations(
     return AugmentationSequence(augmentations)
 
 
+def build_spectrogram_augmentations(
+    steps: Optional[Sequence[SpectrogramAugmentationConfig]] = None,
+) -> Optional[Augmentation]:
+    if not steps:
+        return None
+
+    augmentations = []
+
+    for step_config in steps:
+        augmentation = spec_augmentations.build(step_config)
+
+        if augmentation is None:
+            continue
+
+        augmentations.append(
+            MaybeApply(
+                augmentation=augmentation,
+                probability=step_config.probability,
+            )
+        )
+
+    return AugmentationSequence(augmentations)
+
+
 def build_augmentations(
     samplerate: int,
     config: Optional[AugmentationsConfig] = None,
@@ -675,16 +709,14 @@ def build_augmentations(
         lambda: config.to_yaml_string(),
     )
 
-    audio_augmentation = build_augmentation_sequence(
+    audio_augmentation = build_audio_augmentations(
-        samplerate,
         steps=config.audio,
+        samplerate=samplerate,
         audio_source=audio_source,
     )
 
-    spectrogram_augmentation = build_augmentation_sequence(
+    spectrogram_augmentation = build_spectrogram_augmentations(
-        samplerate,
+        steps=config.spectrogram,
-        steps=config.audio,
-        audio_source=audio_source,
     )
 
     return audio_augmentation, spectrogram_augmentation

src/batdetect2/train/train.py

@@ -146,7 +146,7 @@ def build_trainer_callbacks(
         ModelCheckpoint(
             dirpath=str(checkpoint_dir),
             save_top_k=1,
-            monitor="total_loss/val",
+            monitor="classification/mean_average_precision",
         ),
         ValidationMetrics(evaluator),
     ]