Use pascal voc map computation by default

src/batdetect2/evaluate/metrics.py

@@ -1,4 +1,14 @@
-from typing import Annotated, Dict, List, Literal, Optional, Sequence, Union
+from collections.abc import Callable, Mapping
+from typing import (
+    Annotated,
+    Any,
+    Dict,
+    List,
+    Literal,
+    Optional,
+    Sequence,
+    Union,
+)
 
 import numpy as np
 from pydantic import Field
@@ -16,11 +26,61 @@ __all__ = ["DetectionAP", "ClassificationAP"]
 metrics_registry: Registry[MetricsProtocol, [List[str]]] = Registry("metric")
 
 
+AveragePrecisionImplementation = Literal["sklearn", "pascal_voc"]
+
+
 class DetectionAPConfig(BaseConfig):
     name: Literal["detection_ap"] = "detection_ap"
+    implementation: AveragePrecisionImplementation = "pascal_voc"
+
+
+def pascal_voc_average_precision(y_true, y_score) -> float:
+    y_true = np.array(y_true)
+    y_score = np.array(y_score)
+
+    sort_ind = np.argsort(y_score)[::-1]
+    y_true_sorted = y_true[sort_ind]
+
+    num_positives = y_true.sum()
+    false_pos_c = np.cumsum(1 - y_true_sorted)
+    true_pos_c = np.cumsum(y_true_sorted)
+
+    recall = true_pos_c / num_positives
+    precision = true_pos_c / np.maximum(
+        true_pos_c + false_pos_c,
+        np.finfo(np.float64).eps,
+    )
+
+    precision[np.isnan(precision)] = 0
+    recall[np.isnan(recall)] = 0
+
+    # pascal 12 way
+    mprec = np.hstack((0, precision, 0))
+    mrec = np.hstack((0, recall, 1))
+    for ii in range(mprec.shape[0] - 2, -1, -1):
+        mprec[ii] = np.maximum(mprec[ii], mprec[ii + 1])
+    inds = np.where(np.not_equal(mrec[1:], mrec[:-1]))[0] + 1
+    ave_prec = ((mrec[inds] - mrec[inds - 1]) * mprec[inds]).sum()
+
+    return ave_prec
+
+
+_ap_impl_mapping: Mapping[
+    AveragePrecisionImplementation, Callable[[Any, Any], float]
+] = {
+    "sklearn": metrics.average_precision_score,
+    "pascal_voc": pascal_voc_average_precision,
+}
 
 
 class DetectionAP(MetricsProtocol):
+    def __init__(
+        self,
+        implementation: AveragePrecisionImplementation = "pascal_voc",
+    ):
+        self.implementation = implementation
+        self.metric = _ap_impl_mapping[self.implementation]
+
     def __call__(
         self, clip_evaluations: Sequence[ClipEvaluation]
     ) -> Dict[str, float]:
@@ -31,12 +91,12 @@ class DetectionAP(MetricsProtocol):
                 for match in clip_eval.matches
             ]
         )
-        score = float(metrics.average_precision_score(y_true, y_score))
+        score = float(self.metric(y_true, y_score))
         return {"detection_AP": score}
 
     @classmethod
     def from_config(cls, config: DetectionAPConfig, class_names: List[str]):
-        return cls()
+        return cls(implementation=config.implementation)
 
 
 metrics_registry.register(DetectionAPConfig, DetectionAP)
@@ -52,9 +112,12 @@ class ClassificationAP(MetricsProtocol):
     def __init__(
         self,
         class_names: List[str],
+        implementation: AveragePrecisionImplementation = "pascal_voc",
         include: Optional[List[str]] = None,
         exclude: Optional[List[str]] = None,
     ):
+        self.implementation = implementation
+        self.metric = _ap_impl_mapping[self.implementation]
         self.class_names = class_names
 
         self.selected = class_names
@@ -107,10 +170,7 @@ class ClassificationAP(MetricsProtocol):
         for class_index, class_name in enumerate(self.class_names):
             y_true_class = y_true[:, class_index]
             y_pred_class = y_pred[:, class_index]
-            class_ap = metrics.average_precision_score(
-                y_true_class,
-                y_pred_class,
-            )
+            class_ap = self.metric(y_true_class, y_pred_class)
             class_scores[class_name] = float(class_ap)
 
         mean_ap = np.mean(

src/batdetect2/train/__init__.py

@@ -22,7 +22,14 @@ from batdetect2.train.config import (
     load_full_training_config,
     load_train_config,
 )
-from batdetect2.train.dataset import TrainingDataset
+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
 from batdetect2.train.losses import (
@@ -33,14 +40,7 @@ from batdetect2.train.losses import (
     SizeLossConfig,
     build_loss,
 )
-from batdetect2.train.train import (
-    build_train_dataset,
-    build_train_loader,
-    build_trainer,
-    build_val_dataset,
-    build_val_loader,
-    train,
-)
+from batdetect2.train.train import build_trainer, train
 
 __all__ = [
     "AugmentationsConfig",
@@ -49,7 +49,6 @@ __all__ = [
     "EchoAugmentationConfig",
     "FrequencyMaskAugmentationConfig",
     "FullTrainingConfig",
-    "TrainingDataset",
     "LossConfig",
     "LossFunction",
     "PLTrainerConfig",
@@ -57,7 +56,9 @@ __all__ = [
     "SizeLossConfig",
     "TimeMaskAugmentationConfig",
     "TrainingConfig",
+    "TrainingDataset",
     "TrainingModule",
+    "ValidationDataset",
     "VolumeAugmentationConfig",
     "WarpAugmentationConfig",
     "add_echo",

src/batdetect2/train/config.py

@@ -72,13 +72,16 @@ class TrainLoaderConfig(BaseConfig):
     )
 
 
-class TrainingConfig(BaseConfig):
+class OptimizerConfig(BaseConfig):
     learning_rate: float = 1e-3
     t_max: int = 100
 
+
+class TrainingConfig(BaseConfig):
     train_loader: TrainLoaderConfig = Field(default_factory=TrainLoaderConfig)
     val_loader: ValLoaderConfig = Field(default_factory=ValLoaderConfig)
 
+    optimizer: OptimizerConfig = Field(default_factory=OptimizerConfig)
     loss: LossConfig = Field(default_factory=LossConfig)
     cliping: RandomClipConfig = Field(default_factory=RandomClipConfig)
     trainer: PLTrainerConfig = Field(default_factory=PLTrainerConfig)

src/batdetect2/train/dataset.py

@@ -1,18 +1,31 @@
-from typing import Optional, Sequence, Tuple
+from typing import List, Optional, Sequence
 
 import torch
+from loguru import logger
 from soundevent import data
-from torch.utils.data import Dataset
+from torch.utils.data import DataLoader, Dataset
 
+from batdetect2.plotting.clips import build_audio_loader
+from batdetect2.preprocess import build_preprocessor
+from batdetect2.train.augmentations import (
+    RandomAudioSource,
+    build_augmentations,
+)
+from batdetect2.train.clips import build_clipper
+from batdetect2.train.config import TrainLoaderConfig, ValLoaderConfig
+from batdetect2.train.labels import build_clip_labeler
 from batdetect2.typing import ClipperProtocol, TrainExample
 from batdetect2.typing.preprocess import AudioLoader, PreprocessorProtocol
-from batdetect2.typing.train import (
-    Augmentation,
-    ClipLabeller,
-)
+from batdetect2.typing.train import Augmentation, ClipLabeller
+from batdetect2.utils.arrays import adjust_width
 
 __all__ = [
     "TrainingDataset",
+    "ValidationDataset",
+    "build_val_loader",
+    "build_train_loader",
+    "build_train_dataset",
+    "build_val_dataset",
 ]
 
 
@@ -124,3 +137,174 @@ class ValidationDataset(Dataset):
             start_time=torch.tensor(clip.start_time),
             end_time=torch.tensor(clip.end_time),
         )
+
+
+def build_train_loader(
+    clip_annotations: Sequence[data.ClipAnnotation],
+    audio_loader: Optional[AudioLoader] = None,
+    labeller: Optional[ClipLabeller] = None,
+    preprocessor: Optional[PreprocessorProtocol] = None,
+    config: Optional[TrainLoaderConfig] = None,
+    num_workers: Optional[int] = None,
+) -> DataLoader:
+    config = config or TrainLoaderConfig()
+
+    logger.info("Building training data loader...")
+    logger.opt(lazy=True).debug(
+        "Training data loader config: \n{config}",
+        config=lambda: config.to_yaml_string(exclude_none=True),
+    )
+
+    train_dataset = build_train_dataset(
+        clip_annotations,
+        audio_loader=audio_loader,
+        labeller=labeller,
+        preprocessor=preprocessor,
+        config=config,
+    )
+
+    num_workers = num_workers or config.num_workers
+    return DataLoader(
+        train_dataset,
+        batch_size=config.batch_size,
+        shuffle=config.shuffle,
+        num_workers=num_workers,
+        collate_fn=_collate_fn,
+    )
+
+
+def build_val_loader(
+    clip_annotations: Sequence[data.ClipAnnotation],
+    audio_loader: Optional[AudioLoader] = None,
+    labeller: Optional[ClipLabeller] = None,
+    preprocessor: Optional[PreprocessorProtocol] = None,
+    config: Optional[ValLoaderConfig] = None,
+    num_workers: Optional[int] = None,
+):
+    logger.info("Building validation data loader...")
+    config = config or ValLoaderConfig()
+    logger.opt(lazy=True).debug(
+        "Validation data loader config: \n{config}",
+        config=lambda: config.to_yaml_string(exclude_none=True),
+    )
+
+    val_dataset = build_val_dataset(
+        clip_annotations,
+        audio_loader=audio_loader,
+        labeller=labeller,
+        preprocessor=preprocessor,
+        config=config,
+    )
+
+    num_workers = num_workers or config.num_workers
+    return DataLoader(
+        val_dataset,
+        batch_size=1,
+        shuffle=False,
+        num_workers=num_workers,
+        collate_fn=_collate_fn,
+    )
+
+
+def build_train_dataset(
+    clip_annotations: Sequence[data.ClipAnnotation],
+    audio_loader: Optional[AudioLoader] = None,
+    labeller: Optional[ClipLabeller] = None,
+    preprocessor: Optional[PreprocessorProtocol] = None,
+    config: Optional[TrainLoaderConfig] = None,
+) -> TrainingDataset:
+    logger.info("Building training dataset...")
+    config = config or TrainLoaderConfig()
+
+    clipper = build_clipper(config=config.clipping_strategy)
+
+    if audio_loader is None:
+        audio_loader = build_audio_loader()
+
+    if preprocessor is None:
+        preprocessor = build_preprocessor()
+
+    if labeller is None:
+        labeller = build_clip_labeler(
+            min_freq=preprocessor.min_freq,
+            max_freq=preprocessor.max_freq,
+        )
+
+    random_example_source = RandomAudioSource(
+        clip_annotations,
+        audio_loader=audio_loader,
+    )
+
+    if config.augmentations.enabled:
+        audio_augmentation, spectrogram_augmentation = build_augmentations(
+            samplerate=preprocessor.input_samplerate,
+            config=config.augmentations,
+            audio_source=random_example_source,
+        )
+    else:
+        logger.debug("No augmentations configured for training dataset.")
+        audio_augmentation = None
+        spectrogram_augmentation = None
+
+    return TrainingDataset(
+        clip_annotations,
+        audio_loader=audio_loader,
+        labeller=labeller,
+        clipper=clipper,
+        preprocessor=preprocessor,
+        audio_augmentation=audio_augmentation,
+        spectrogram_augmentation=spectrogram_augmentation,
+    )
+
+
+def build_val_dataset(
+    clip_annotations: Sequence[data.ClipAnnotation],
+    audio_loader: Optional[AudioLoader] = None,
+    labeller: Optional[ClipLabeller] = None,
+    preprocessor: Optional[PreprocessorProtocol] = None,
+    config: Optional[ValLoaderConfig] = None,
+) -> ValidationDataset:
+    logger.info("Building validation dataset...")
+    config = config or ValLoaderConfig()
+
+    if audio_loader is None:
+        audio_loader = build_audio_loader()
+
+    if preprocessor is None:
+        preprocessor = build_preprocessor()
+
+    if labeller is None:
+        labeller = build_clip_labeler(
+            min_freq=preprocessor.min_freq,
+            max_freq=preprocessor.max_freq,
+        )
+
+    clipper = build_clipper(config.clipping_strategy)
+    return ValidationDataset(
+        clip_annotations,
+        audio_loader=audio_loader,
+        labeller=labeller,
+        preprocessor=preprocessor,
+        clipper=clipper,
+    )
+
+
+def _collate_fn(batch: List[TrainExample]) -> TrainExample:
+    max_width = max(item.spec.shape[-1] for item in batch)
+    return TrainExample(
+        spec=torch.stack(
+            [adjust_width(item.spec, max_width) for item in batch]
+        ),
+        detection_heatmap=torch.stack(
+            [adjust_width(item.detection_heatmap, max_width) for item in batch]
+        ),
+        size_heatmap=torch.stack(
+            [adjust_width(item.size_heatmap, max_width) for item in batch]
+        ),
+        class_heatmap=torch.stack(
+            [adjust_width(item.class_heatmap, max_width) for item in batch]
+        ),
+        idx=torch.stack([item.idx for item in batch]),
+        start_time=torch.stack([item.start_time for item in batch]),
+        end_time=torch.stack([item.end_time for item in batch]),
+    )

src/batdetect2/train/lightning.py

@@ -77,3 +77,15 @@ def load_model_from_checkpoint(
 ) -> Tuple[Model, FullTrainingConfig]:
     module = TrainingModule.load_from_checkpoint(path)  # type: ignore
     return module.model, module.config
+
+
+def build_training_module(
+    config: Optional[FullTrainingConfig] = None,
+    t_max: int = 200,
+) -> TrainingModule:
+    config = config or FullTrainingConfig()
+    return TrainingModule(
+        config=config,
+        learning_rate=config.train.optimizer.learning_rate,
+        t_max=t_max,
+    )

src/batdetect2/train/train.py

@@ -2,47 +2,31 @@ from collections.abc import Sequence
 from pathlib import Path
 from typing import List, Optional
 
-import torch
 from lightning import Trainer, seed_everything
 from lightning.pytorch.callbacks import Callback, ModelCheckpoint
 from loguru import logger
 from soundevent import data
-from torch.utils.data import DataLoader
 
-from batdetect2.evaluate.config import EvaluationConfig
 from batdetect2.evaluate.evaluator import build_evaluator
-from batdetect2.plotting.clips import AudioLoader, build_audio_loader
+from batdetect2.plotting.clips import PreprocessorProtocol, build_audio_loader
 from batdetect2.preprocess import build_preprocessor
 from batdetect2.targets import build_targets
-from batdetect2.train.augmentations import (
-    RandomAudioSource,
-    build_augmentations,
-)
 from batdetect2.train.callbacks import ValidationMetrics
-from batdetect2.train.clips import build_clipper
 from batdetect2.train.config import (
     FullTrainingConfig,
-    TrainLoaderConfig,
-    ValLoaderConfig,
 )
-from batdetect2.train.dataset import TrainingDataset, ValidationDataset
+from batdetect2.train.dataset import build_train_loader, build_val_loader
 from batdetect2.train.labels import build_clip_labeler
-from batdetect2.train.lightning import TrainingModule
+from batdetect2.train.lightning import TrainingModule, build_training_module
 from batdetect2.train.logging import build_logger
 from batdetect2.typing import (
-    PreprocessorProtocol,
     TargetProtocol,
-    TrainExample,
 )
+from batdetect2.typing.preprocess import AudioLoader
 from batdetect2.typing.train import ClipLabeller
-from batdetect2.utils.arrays import adjust_width
 
 __all__ = [
-    "build_train_dataset",
-    "build_train_loader",
     "build_trainer",
-    "build_val_dataset",
-    "build_val_loader",
     "train",
 ]
 
@@ -52,6 +36,11 @@ DEFAULT_CHECKPOINT_DIR: Path = Path("outputs") / "checkpoints"
 def train(
     train_annotations: Sequence[data.ClipAnnotation],
     val_annotations: Optional[Sequence[data.ClipAnnotation]] = None,
+    trainer: Optional[Trainer] = None,
+    targets: Optional[TargetProtocol] = None,
+    preprocessor: Optional[PreprocessorProtocol] = None,
+    audio_loader: Optional[AudioLoader] = None,
+    labeller: Optional[ClipLabeller] = None,
     config: Optional[FullTrainingConfig] = None,
     model_path: Optional[data.PathLike] = None,
     train_workers: Optional[int] = None,
@@ -67,13 +56,15 @@ def train(
 
     config = config or FullTrainingConfig()
 
-    targets = build_targets(config.targets)
+    targets = targets or build_targets(config.targets)
 
-    preprocessor = build_preprocessor(config.preprocess)
+    preprocessor = preprocessor or build_preprocessor(config.preprocess)
 
-    audio_loader = build_audio_loader(config=config.preprocess.audio)
+    audio_loader = audio_loader or build_audio_loader(
+        config=config.preprocess.audio
+    )
 
-    labeller = build_clip_labeler(
+    labeller = labeller or build_clip_labeler(
         targets,
         min_freq=preprocessor.min_freq,
         max_freq=preprocessor.max_freq,
@@ -108,10 +99,10 @@ def train(
     else:
         module = build_training_module(
             config,
-            t_max=config.train.t_max * len(train_dataloader),
+            t_max=config.train.optimizer.t_max * len(train_dataloader),
         )
 
-    trainer = build_trainer(
+    trainer = trainer or build_trainer(
         config,
         targets=targets,
         checkpoint_dir=checkpoint_dir,
@@ -129,21 +120,9 @@ def train(
     logger.info("Training complete.")
 
 
-def build_training_module(
-    config: Optional[FullTrainingConfig] = None,
-    t_max: int = 200,
-) -> TrainingModule:
-    config = config or FullTrainingConfig()
-    return TrainingModule(
-        config=config,
-        learning_rate=config.train.learning_rate,
-        t_max=t_max,
-    )
-
-
 def build_trainer_callbacks(
     targets: TargetProtocol,
-    config: EvaluationConfig,
+    config: FullTrainingConfig,
     checkpoint_dir: Optional[Path] = None,
     experiment_name: Optional[str] = None,
     run_name: Optional[str] = None,
@@ -157,7 +136,7 @@ def build_trainer_callbacks(
     if run_name is not None:
         checkpoint_dir = checkpoint_dir / run_name
 
-    evaluator = build_evaluator(config=config, targets=targets)
+    evaluator = build_evaluator(config=config.evaluation, targets=targets)
 
     return [
         ModelCheckpoint(
@@ -202,180 +181,9 @@ def build_trainer(
         logger=train_logger,
         callbacks=build_trainer_callbacks(
             targets,
-            config=conf.evaluation,
+            config=conf,
             checkpoint_dir=checkpoint_dir,
             experiment_name=experiment_name,
             run_name=run_name,
         ),
     )
-
-
-def build_train_loader(
-    clip_annotations: Sequence[data.ClipAnnotation],
-    audio_loader: Optional[AudioLoader] = None,
-    labeller: Optional[ClipLabeller] = None,
-    preprocessor: Optional[PreprocessorProtocol] = None,
-    config: Optional[TrainLoaderConfig] = None,
-    num_workers: Optional[int] = None,
-) -> DataLoader:
-    config = config or TrainLoaderConfig()
-
-    logger.info("Building training data loader...")
-    logger.opt(lazy=True).debug(
-        "Training data loader config: \n{config}",
-        config=lambda: config.to_yaml_string(exclude_none=True),
-    )
-
-    train_dataset = build_train_dataset(
-        clip_annotations,
-        audio_loader=audio_loader,
-        labeller=labeller,
-        preprocessor=preprocessor,
-        config=config,
-    )
-
-    num_workers = num_workers or config.num_workers
-    return DataLoader(
-        train_dataset,
-        batch_size=config.batch_size,
-        shuffle=config.shuffle,
-        num_workers=num_workers,
-        collate_fn=_collate_fn,
-    )
-
-
-def build_val_loader(
-    clip_annotations: Sequence[data.ClipAnnotation],
-    audio_loader: Optional[AudioLoader] = None,
-    labeller: Optional[ClipLabeller] = None,
-    preprocessor: Optional[PreprocessorProtocol] = None,
-    config: Optional[ValLoaderConfig] = None,
-    num_workers: Optional[int] = None,
-):
-    logger.info("Building validation data loader...")
-    config = config or ValLoaderConfig()
-    logger.opt(lazy=True).debug(
-        "Validation data loader config: \n{config}",
-        config=lambda: config.to_yaml_string(exclude_none=True),
-    )
-
-    val_dataset = build_val_dataset(
-        clip_annotations,
-        audio_loader=audio_loader,
-        labeller=labeller,
-        preprocessor=preprocessor,
-        config=config,
-    )
-
-    num_workers = num_workers or config.num_workers
-    return DataLoader(
-        val_dataset,
-        batch_size=1,
-        shuffle=False,
-        num_workers=num_workers,
-        collate_fn=_collate_fn,
-    )
-
-
-def build_train_dataset(
-    clip_annotations: Sequence[data.ClipAnnotation],
-    audio_loader: Optional[AudioLoader] = None,
-    labeller: Optional[ClipLabeller] = None,
-    preprocessor: Optional[PreprocessorProtocol] = None,
-    config: Optional[TrainLoaderConfig] = None,
-) -> TrainingDataset:
-    logger.info("Building training dataset...")
-    config = config or TrainLoaderConfig()
-
-    clipper = build_clipper(config=config.clipping_strategy)
-
-    if audio_loader is None:
-        audio_loader = build_audio_loader()
-
-    if preprocessor is None:
-        preprocessor = build_preprocessor()
-
-    if labeller is None:
-        labeller = build_clip_labeler(
-            min_freq=preprocessor.min_freq,
-            max_freq=preprocessor.max_freq,
-        )
-
-    random_example_source = RandomAudioSource(
-        clip_annotations,
-        audio_loader=audio_loader,
-    )
-
-    if config.augmentations.enabled:
-        audio_augmentation, spectrogram_augmentation = build_augmentations(
-            samplerate=preprocessor.input_samplerate,
-            config=config.augmentations,
-            audio_source=random_example_source,
-        )
-    else:
-        logger.debug("No augmentations configured for training dataset.")
-        audio_augmentation = None
-        spectrogram_augmentation = None
-
-    return TrainingDataset(
-        clip_annotations,
-        audio_loader=audio_loader,
-        labeller=labeller,
-        clipper=clipper,
-        preprocessor=preprocessor,
-        audio_augmentation=audio_augmentation,
-        spectrogram_augmentation=spectrogram_augmentation,
-    )
-
-
-def build_val_dataset(
-    clip_annotations: Sequence[data.ClipAnnotation],
-    audio_loader: Optional[AudioLoader] = None,
-    labeller: Optional[ClipLabeller] = None,
-    preprocessor: Optional[PreprocessorProtocol] = None,
-    config: Optional[ValLoaderConfig] = None,
-) -> ValidationDataset:
-    logger.info("Building validation dataset...")
-    config = config or ValLoaderConfig()
-
-    if audio_loader is None:
-        audio_loader = build_audio_loader()
-
-    if preprocessor is None:
-        preprocessor = build_preprocessor()
-
-    if labeller is None:
-        labeller = build_clip_labeler(
-            min_freq=preprocessor.min_freq,
-            max_freq=preprocessor.max_freq,
-        )
-
-    clipper = build_clipper(config.clipping_strategy)
-    return ValidationDataset(
-        clip_annotations,
-        audio_loader=audio_loader,
-        labeller=labeller,
-        preprocessor=preprocessor,
-        clipper=clipper,
-    )
-
-
-def _collate_fn(batch: List[TrainExample]) -> TrainExample:
-    max_width = max(item.spec.shape[-1] for item in batch)
-    return TrainExample(
-        spec=torch.stack(
-            [adjust_width(item.spec, max_width) for item in batch]
-        ),
-        detection_heatmap=torch.stack(
-            [adjust_width(item.detection_heatmap, max_width) for item in batch]
-        ),
-        size_heatmap=torch.stack(
-            [adjust_width(item.size_heatmap, max_width) for item in batch]
-        ),
-        class_heatmap=torch.stack(
-            [adjust_width(item.class_heatmap, max_width) for item in batch]
-        ),
-        idx=torch.stack([item.idx for item in batch]),
-        start_time=torch.stack([item.start_time for item in batch]),
-        end_time=torch.stack([item.end_time for item in batch]),
-    )