Add checkpoint config

src/batdetect2/train/checkpoints.py

@@ -0,0 +1,47 @@
+from pathlib import Path
+from typing import Optional
+
+from lightning.pytorch.callbacks import Callback, ModelCheckpoint
+
+from batdetect2.core import BaseConfig
+
+__all__ = [
+    "CheckpointConfig",
+    "build_checkpoint_callback",
+]
+
+DEFAULT_CHECKPOINT_DIR: Path = Path("outputs") / "checkpoints"
+
+
+class CheckpointConfig(BaseConfig):
+    checkpoint_dir: Path = DEFAULT_CHECKPOINT_DIR
+    monitor: str = "classification/mean_average_precision"
+    mode: str = "max"
+    save_top_k: int = 1
+    filename: Optional[str] = None
+
+
+def build_checkpoint_callback(
+    config: Optional[CheckpointConfig] = None,
+    checkpoint_dir: Optional[Path] = None,
+    experiment_name: Optional[str] = None,
+    run_name: Optional[str] = None,
+) -> Callback:
+    config = config or CheckpointConfig()
+
+    if checkpoint_dir is None:
+        checkpoint_dir = config.checkpoint_dir
+
+    if experiment_name is not None:
+        checkpoint_dir = checkpoint_dir / experiment_name
+
+    if run_name is not None:
+        checkpoint_dir = checkpoint_dir / run_name
+
+    return ModelCheckpoint(
+        dirpath=str(checkpoint_dir),
+        save_top_k=config.save_top_k,
+        monitor=config.monitor,
+        mode=config.mode,
+        filename=config.filename,
+    )

src/batdetect2/train/config.py

@@ -6,6 +6,7 @@ from soundevent import data
 from batdetect2.core.configs import BaseConfig, load_config
 from batdetect2.evaluate.config import EvaluationConfig
 from batdetect2.logging import LoggerConfig, TensorBoardLoggerConfig
+from batdetect2.train.checkpoints import CheckpointConfig
 from batdetect2.train.dataset import TrainLoaderConfig, ValLoaderConfig
 from batdetect2.train.labels import LabelConfig
 from batdetect2.train.losses import LossConfig
@@ -51,6 +52,7 @@ class TrainingConfig(BaseConfig):
     logger: LoggerConfig = Field(default_factory=TensorBoardLoggerConfig)
     labels: LabelConfig = Field(default_factory=LabelConfig)
     validation: EvaluationConfig = Field(default_factory=EvaluationConfig)
+    checkpoints: CheckpointConfig = Field(default_factory=CheckpointConfig)
 
 
 def load_train_config(

src/batdetect2/train/train.py

@@ -1,9 +1,8 @@
 from collections.abc import Sequence
 from pathlib import Path
-from typing import TYPE_CHECKING, List, Optional
+from typing import TYPE_CHECKING, Optional
 
 from lightning import Trainer, seed_everything
-from lightning.pytorch.callbacks import Callback, ModelCheckpoint
 from loguru import logger
 from soundevent import data
 
@@ -13,6 +12,7 @@ from batdetect2.logging import build_logger
 from batdetect2.preprocess import build_preprocessor
 from batdetect2.targets import build_targets
 from batdetect2.train.callbacks import ValidationMetrics
+from batdetect2.train.checkpoints import build_checkpoint_callback
 from batdetect2.train.dataset import build_train_loader, build_val_loader
 from batdetect2.train.labels import build_clip_labeler
 from batdetect2.train.lightning import build_training_module
@@ -32,8 +32,6 @@ __all__ = [
     "train",
 ]
 
-DEFAULT_CHECKPOINT_DIR: Path = Path("outputs") / "checkpoints"
-
 
 def train(
     train_annotations: Sequence[data.ClipAnnotation],
@@ -104,7 +102,6 @@ def train(
 
     trainer = trainer or build_trainer(
         config,
-        targets=targets,
         evaluator=build_evaluator(
             config.train.validation,
             targets=targets,
@@ -124,58 +121,29 @@ def train(
     logger.info("Training complete.")
 
 
-def build_trainer_callbacks(
-    targets: "TargetProtocol",
-    evaluator: Optional["EvaluatorProtocol"] = None,
-    checkpoint_dir: Optional[Path] = None,
-    experiment_name: Optional[str] = None,
-    run_name: Optional[str] = None,
-) -> List[Callback]:
-    if checkpoint_dir is None:
-        checkpoint_dir = DEFAULT_CHECKPOINT_DIR
-
-    if experiment_name is not None:
-        checkpoint_dir = checkpoint_dir / experiment_name
-
-    if run_name is not None:
-        checkpoint_dir = checkpoint_dir / run_name
-
-    evaluator = evaluator or build_evaluator(targets=targets)
-
-    return [
-        ModelCheckpoint(
-            dirpath=str(checkpoint_dir),
-            save_top_k=1,
-            monitor="classification/mean_average_precision",
-            mode="max",
-        ),
-        ValidationMetrics(evaluator),
-    ]
-
-
 def build_trainer(
-    conf: "BatDetect2Config",
+    config: "BatDetect2Config",
-    targets: "TargetProtocol",
+    evaluator: "EvaluatorProtocol",
-    evaluator: Optional["EvaluatorProtocol"] = None,
     checkpoint_dir: Optional[Path] = None,
     log_dir: Optional[Path] = None,
     experiment_name: Optional[str] = None,
     run_name: Optional[str] = None,
 ) -> Trainer:
-    trainer_conf = conf.train.trainer
+    trainer_conf = config.train.trainer
     logger.opt(lazy=True).debug(
         "Building trainer with config: \n{config}",
         config=lambda: trainer_conf.to_yaml_string(exclude_none=True),
     )
+
     train_logger = build_logger(
-        conf.train.logger,
+        config.train.logger,
         log_dir=log_dir,
         experiment_name=experiment_name,
         run_name=run_name,
     )
 
     train_logger.log_hyperparams(
-        conf.model_dump(
+        config.model_dump(
             mode="json",
             exclude_none=True,
         )
@@ -184,11 +152,13 @@ def build_trainer(
     return Trainer(
         **trainer_conf.model_dump(exclude_none=True),
         logger=train_logger,
-        callbacks=build_trainer_callbacks(
+        callbacks=[
-            targets,
+            build_checkpoint_callback(
-            evaluator=evaluator,
+                config=config.train.checkpoints,
                 checkpoint_dir=checkpoint_dir,
                 experiment_name=experiment_name,
                 run_name=run_name,
             ),
+            ValidationMetrics(evaluator),
+        ],
     )