api_v2

src/batdetect2/api_v2.py

@@ -8,13 +8,18 @@ from soundevent.audio.files import get_audio_files
 
 from batdetect2.audio import build_audio_loader
 from batdetect2.config import BatDetect2Config
-from batdetect2.evaluate import build_evaluator, evaluate
+from batdetect2.evaluate import DEFAULT_EVAL_DIR, build_evaluator, evaluate
 from batdetect2.inference import process_file_list, run_batch_inference
+from batdetect2.logging import DEFAULT_LOGS_DIR
 from batdetect2.models import Model, build_model
 from batdetect2.postprocess import build_postprocessor, to_raw_predictions
 from batdetect2.preprocess import build_preprocessor
 from batdetect2.targets import build_targets
-from batdetect2.train import load_model_from_checkpoint, train
+from batdetect2.train import (
+    DEFAULT_CHECKPOINT_DIR,
+    load_model_from_checkpoint,
+    train,
+)
 from batdetect2.typing import (
     AudioLoader,
     BatDetect2Prediction,
@@ -53,8 +58,8 @@ class BatDetect2API:
         val_annotations: Optional[Sequence[data.ClipAnnotation]] = None,
         train_workers: Optional[int] = None,
         val_workers: Optional[int] = None,
-        checkpoint_dir: Optional[Path] = None,
-        log_dir: Optional[Path] = None,
+        checkpoint_dir: Optional[Path] = DEFAULT_CHECKPOINT_DIR,
+        log_dir: Optional[Path] = DEFAULT_LOGS_DIR,
         experiment_name: Optional[str] = None,
         run_name: Optional[str] = None,
         seed: Optional[int] = None,
@@ -80,7 +85,7 @@ class BatDetect2API:
         self,
         test_annotations: Sequence[data.ClipAnnotation],
         num_workers: Optional[int] = None,
-        output_dir: data.PathLike = ".",
+        output_dir: data.PathLike = DEFAULT_EVAL_DIR,
         experiment_name: Optional[str] = None,
         run_name: Optional[str] = None,
     ):

src/batdetect2/evaluate/__init__.py

@@ -1,5 +1,5 @@
 from batdetect2.evaluate.config import EvaluationConfig, load_evaluation_config
-from batdetect2.evaluate.evaluate import evaluate
+from batdetect2.evaluate.evaluate import DEFAULT_EVAL_DIR, evaluate
 from batdetect2.evaluate.evaluator import Evaluator, build_evaluator
 from batdetect2.evaluate.tasks import TaskConfig, build_task
 
@@ -11,4 +11,5 @@ __all__ = [
     "build_task",
     "evaluate",
     "load_evaluation_config",
+    "DEFAULT_EVAL_DIR",
 ]

src/batdetect2/evaluate/evaluate.py

@@ -21,7 +21,7 @@ if TYPE_CHECKING:
         TargetProtocol,
     )
 
-DEFAULT_OUTPUT_DIR: Path = Path("outputs") / "evaluations"
+DEFAULT_EVAL_DIR: Path = Path("outputs") / "evaluations"
 
 
 def evaluate(
@@ -32,7 +32,7 @@ def evaluate(
     preprocessor: Optional["PreprocessorProtocol"] = None,
     config: Optional["BatDetect2Config"] = None,
     num_workers: Optional[int] = None,
-    output_dir: data.PathLike = DEFAULT_OUTPUT_DIR,
+    output_dir: data.PathLike = DEFAULT_EVAL_DIR,
     experiment_name: Optional[str] = None,
     run_name: Optional[str] = None,
 ):

src/batdetect2/evaluate/metrics/common.py

@@ -43,6 +43,9 @@ def average_precision(
     y_score,
     num_positives: Optional[int] = None,
 ) -> float:
+    if num_positives == 0:
+        return np.nan
+
     precision, recall, _ = compute_precision_recall(
         y_true,
         y_score,

src/batdetect2/inference/batch.py

@@ -51,7 +51,7 @@ def run_batch_inference(
     )
 
     module = InferenceModule(model)
-    trainer = Trainer(enable_checkpointing=False)
+    trainer = Trainer(enable_checkpointing=False, logger=False)
     outputs = trainer.predict(module, loader)
     return [
         clip_prediction

src/batdetect2/logging.py

@@ -221,20 +221,16 @@ def build_logger(
     experiment_name: Optional[str] = None,
     run_name: Optional[str] = None,
 ) -> Logger:
-    """
-    Creates a logger instance from a validated Pydantic config object.
-    """
     logger.opt(lazy=True).debug(
         "Building logger with config: \n{}",
         lambda: config.to_yaml_string(),
     )
-    logger_type = config.name
 
+    logger_type = config.name
     if logger_type not in LOGGER_FACTORY:
         raise ValueError(f"Unknown logger type: {logger_type}")
 
     creation_func = LOGGER_FACTORY[logger_type]
-
     return creation_func(
         config,
         log_dir=log_dir,

src/batdetect2/train/__init__.py

@@ -1,81 +1,19 @@
-from batdetect2.train.augmentations import (
-    AddEchoConfig,
-    AugmentationsConfig,
-    MaskFrequencyConfig,
-    MaskTimeConfig,
-    RandomAudioSource,
-    ScaleVolumeConfig,
-    WarpConfig,
-    add_echo,
-    build_augmentations,
-    mask_frequency,
-    mask_time,
-    mix_audio,
-    scale_volume,
-    warp_spectrogram,
-)
 from batdetect2.train.config import (
-    PLTrainerConfig,
     TrainingConfig,
     load_train_config,
 )
-from batdetect2.train.dataset import (
-    TrainingDataset,
-    ValidationDataset,
-    build_train_dataset,
-    build_train_loader,
-    build_val_dataset,
-    build_val_loader,
-)
-from batdetect2.train.labels import build_clip_labeler, load_label_config
 from batdetect2.train.lightning import (
     TrainingModule,
     load_model_from_checkpoint,
 )
-from batdetect2.train.losses import (
-    ClassificationLossConfig,
-    DetectionLossConfig,
-    LossConfig,
-    LossFunction,
-    SizeLossConfig,
-    build_loss,
-)
-from batdetect2.train.train import build_trainer, train
+from batdetect2.train.train import DEFAULT_CHECKPOINT_DIR, build_trainer, train
 
 __all__ = [
-    "AugmentationsConfig",
-    "ClassificationLossConfig",
-    "DetectionLossConfig",
-    "AddEchoConfig",
-    "MaskFrequencyConfig",
-    "LossConfig",
-    "LossFunction",
-    "PLTrainerConfig",
-    "RandomAudioSource",
-    "SizeLossConfig",
-    "MaskTimeConfig",
+    "DEFAULT_CHECKPOINT_DIR",
     "TrainingConfig",
-    "TrainingDataset",
     "TrainingModule",
-    "ValidationDataset",
-    "ScaleVolumeConfig",
-    "WarpConfig",
-    "add_echo",
-    "build_augmentations",
-    "build_clip_labeler",
-    "build_loss",
-    "build_train_dataset",
-    "build_train_loader",
     "build_trainer",
-    "build_val_dataset",
-    "build_val_loader",
-    "load_label_config",
-    "load_train_config",
-    "mask_frequency",
-    "mask_time",
-    "mix_audio",
-    "scale_volume",
-    "train",
-    "warp_spectrogram",
     "load_model_from_checkpoint",
+    "load_train_config",
+    "train",
 ]