Make matching in parallel for speedup

src/batdetect2/evaluate/match.py

@@ -1,5 +1,6 @@
 from collections.abc import Callable, Iterable, Mapping
 from dataclasses import dataclass, field
+from functools import partial
 from typing import List, Literal, Optional, Protocol, Tuple
 
 import numpy as np
@@ -8,6 +9,7 @@ from soundevent import data
 from soundevent.evaluation import compute_affinity
 from soundevent.evaluation import match_geometries as optimal_match
 from soundevent.geometry import compute_bounds
+from torch.multiprocessing import Pool
 
 from batdetect2.configs import BaseConfig
 from batdetect2.typing import (
@@ -428,19 +430,17 @@ def match_all_predictions(
     config: Optional[MatchConfig] = None,
 ) -> List[MatchEvaluation]:
     logger.info("Matching all annotations and predictions...")
-    return [
-        match
-        for clip_annotation, raw_predictions in zip(
-            clip_annotations,
-            predictions,
+    with Pool() as p:
+        all_matches = p.starmap(
+            partial(
+                match_sound_events_and_raw_predictions,
+                targets=targets,
+                config=config,
+            ),
+            zip(clip_annotations, predictions),
         )
-        for match in match_sound_events_and_raw_predictions(
-            clip_annotation,
-            raw_predictions,
-            targets=targets,
-            config=config,
-        )
-    ]
+
+    return [match for matches in all_matches for match in matches]
 
 
 @dataclass

src/batdetect2/train/callbacks.py

@@ -108,7 +108,8 @@ class ValidationMetrics(Callback):
         trainer: Trainer,
         pl_module: LightningModule,
     ) -> None:
-        self._matches = []
+        self._clip_annotations = []
+        self._predictions = []
         return super().on_validation_epoch_start(trainer, pl_module)
 
     def on_validation_batch_end(  # type: ignore