Create dataloader config

src/batdetect2/cli/train.py

@@ -22,8 +22,8 @@ __all__ = ["train_command"]
 @click.option("--model-path", type=click.Path(exists=True))
 @click.option("--config", type=click.Path(exists=True))
 @click.option("--config-field", type=str)
-@click.option("--train-workers", type=int, default=0)
-@click.option("--val-workers", type=int, default=0)
+@click.option("--train-workers", type=int)
+@click.option("--val-workers", type=int)
 @click.option(
     "-v",
     "--verbose",

src/batdetect2/train/config.py

@@ -45,10 +45,29 @@ class PLTrainerConfig(BaseConfig):
     val_check_interval: Optional[Union[int, float]] = None
 
 
-class TrainingConfig(PLTrainerConfig):
-    batch_size: int = 8
+class DataLoaderConfig(BaseConfig):
+    batch_size: int
+    shuffle: bool
+    num_workers: int = 0
+
+
+DEFAULT_TRAIN_LOADER_CONFIG = DataLoaderConfig(batch_size=8, shuffle=True)
+DEFAULT_VAL_LOADER_CONFIG = DataLoaderConfig(batch_size=8, shuffle=False)
+
+
+class LoadersConfig(BaseConfig):
+    train: DataLoaderConfig = Field(
+        default_factory=lambda: DEFAULT_TRAIN_LOADER_CONFIG.model_copy()
+    )
+    val: DataLoaderConfig = Field(
+        default_factory=lambda: DEFAULT_VAL_LOADER_CONFIG.model_copy()
+    )
+
+
+class TrainingConfig(BaseConfig):
     learning_rate: float = 1e-3
     t_max: int = 100
+    dataloaders: LoadersConfig = Field(default_factory=LoadersConfig)
     loss: LossConfig = Field(default_factory=LossConfig)
     augmentations: Optional[AugmentationsConfig] = Field(
         default_factory=lambda: DEFAULT_AUGMENTATION_CONFIG

src/batdetect2/train/logging.py

@@ -93,7 +93,6 @@ def create_tensorboard_logger(config: TensorBoardLoggerConfig) -> Logger:
         name=config.name,
         version=config.version,
         log_graph=config.log_graph,
-        flush_logs_every_n_steps=config.flush_logs_every_n_steps,
     )
 
 

src/batdetect2/train/train.py

@@ -1,7 +1,6 @@
 from collections.abc import Sequence
 from typing import List, Optional
 
-import yaml
 from lightning import Trainer
 from lightning.pytorch.callbacks import Callback, ModelCheckpoint
 from loguru import logger
@@ -20,11 +19,7 @@ from batdetect2.targets import TargetProtocol
 from batdetect2.train.augmentations import build_augmentations
 from batdetect2.train.callbacks import ValidationMetrics
 from batdetect2.train.clips import build_clipper
-from batdetect2.train.config import (
-    FullTrainingConfig,
-    PLTrainerConfig,
-    TrainingConfig,
-)
+from batdetect2.train.config import FullTrainingConfig, TrainingConfig
 from batdetect2.train.dataset import (
     LabeledDataset,
     RandomExampleSource,
@@ -48,8 +43,8 @@ def train(
     val_examples: Optional[Sequence[data.PathLike]] = None,
     config: Optional[FullTrainingConfig] = None,
     model_path: Optional[data.PathLike] = None,
-    train_workers: int = 0,
-    val_workers: int = 0,
+    train_workers: Optional[int] = None,
+    val_workers: Optional[int] = None,
 ):
     conf = config or FullTrainingConfig()
 
@@ -110,16 +105,13 @@ def build_trainer(
     conf: FullTrainingConfig,
     targets: TargetProtocol,
 ) -> Trainer:
-    trainer_conf = PLTrainerConfig.model_validate(
-        conf.train.model_dump(mode="python")
-    )
+    trainer_conf = conf.train.trainer
     logger.opt(lazy=True).debug(
         "Building trainer with config: \n{config}",
         config=lambda: trainer_conf.to_yaml_string(exclude_none=True),
     )
     return Trainer(
         **trainer_conf.model_dump(exclude_none=True),
-        val_check_interval=conf.train.val_check_interval,
         logger=build_logger(conf.train.logger),
         callbacks=build_trainer_callbacks(targets),
     )
@@ -137,22 +129,17 @@ def build_train_loader(
         preprocessor=preprocessor,
         config=config,
     )
+    loader_conf = config.dataloaders.train
     logger.opt(lazy=True).debug(
-        "Training data loader config: \n{}",
-        lambda: yaml.dump(
-            {
-                "batch_size": config.batch_size,
-                "shuffle": True,
-                "num_workers": num_workers or 0,
-                "collate_fn": str(collate_fn),
-            }
-        ),
+        "Training data loader config: \n{config}",
+        config=loader_conf.to_yaml_string(exclude_none=True),
     )
+    num_workers = num_workers or loader_conf.num_workers
     return DataLoader(
         train_dataset,
-        batch_size=config.batch_size,
-        shuffle=True,
-        num_workers=num_workers or 0,
+        batch_size=loader_conf.batch_size,
+        shuffle=loader_conf.shuffle,
+        num_workers=num_workers,
         collate_fn=collate_fn,
     )
 
@@ -167,22 +154,17 @@ def build_val_loader(
         val_examples,
         config=config,
     )
+    loader_conf = config.dataloaders.val
     logger.opt(lazy=True).debug(
-        "Validation data loader config: \n{}",
-        lambda: yaml.dump(
-            {
-                "batch_size": config.batch_size,
-                "shuffle": False,
-                "num_workers": num_workers or 0,
-                "collate_fn": str(collate_fn),
-            }
-        ),
+        "Validation data loader config: \n{config}",
+        config=loader_conf.to_yaml_string(exclude_none=True),
     )
+    num_workers = num_workers or loader_conf.num_workers
     return DataLoader(
         val_dataset,
-        batch_size=config.batch_size,
-        shuffle=False,
-        num_workers=num_workers or 0,
+        batch_size=loader_conf.batch_size,
+        shuffle=loader_conf.shuffle,
+        num_workers=num_workers,
         collate_fn=collate_fn,
     )