Moving types around to each submodule

.gitignore

@@ -102,7 +102,7 @@ experiments/*
 DvcLiveLogger/checkpoints
 logs/
 mlruns/
-outputs/
+/outputs/
 notebooks/lightning_logs
 
 # Jupiter notebooks

docs/source/architecture.md

@@ -89,5 +89,5 @@ Crucial for training, this module translates physical annotations (Regions of In
 ## Summary
 To navigate this codebase effectively:
 1. Follow **`api_v2.py`** to see how high-level operations invoke individual components.
-2. Rely heavily on the typed **Protocols** located in `src/batdetect2/typing/` to understand the inputs and outputs of each subsystem without needing to read the specific implementations.
+2. Rely heavily on the typed **Protocols** located in each subsystem's `types.py` module (for example `src/batdetect2/preprocess/types.py` and `src/batdetect2/postprocess/types.py`) to understand inputs and outputs without needing to read each implementation.
-3. Understand that data flows structurally as `soundevent` primitives externally, and as pure `torch.Tensor` internally through the network.
+3. Understand that data flows structurally as `soundevent` primitives externally, and as pure `torch.Tensor` internally through the network.

src/batdetect2/api_v2.py

@@ -8,6 +8,7 @@ from soundevent import data
 from soundevent.audio.files import get_audio_files
 
 from batdetect2.audio import build_audio_loader
+from batdetect2.audio.types import AudioLoader
 from batdetect2.config import BatDetect2Config
 from batdetect2.core import merge_configs
 from batdetect2.data import (
@@ -15,6 +16,7 @@ from batdetect2.data import (
 )
 from batdetect2.data.datasets import Dataset
 from batdetect2.evaluate import DEFAULT_EVAL_DIR, build_evaluator, evaluate
+from batdetect2.evaluate.types import EvaluatorProtocol
 from batdetect2.inference import process_file_list, run_batch_inference
 from batdetect2.logging import DEFAULT_LOGS_DIR
 from batdetect2.models import Model, build_model
@@ -25,24 +27,22 @@ from batdetect2.outputs import (
     build_output_transform,
     get_output_formatter,
 )
+from batdetect2.outputs.types import OutputFormatterProtocol
 from batdetect2.postprocess import build_postprocessor, to_raw_predictions
+from batdetect2.postprocess.types import (
+    ClipDetections,
+    Detection,
+    PostprocessorProtocol,
+)
 from batdetect2.preprocess import build_preprocessor
+from batdetect2.preprocess.types import PreprocessorProtocol
 from batdetect2.targets import build_targets
+from batdetect2.targets.types import TargetProtocol
 from batdetect2.train import (
     DEFAULT_CHECKPOINT_DIR,
     load_model_from_checkpoint,
     run_train,
 )
-from batdetect2.typing import (
-    AudioLoader,
-    ClipDetections,
-    Detection,
-    EvaluatorProtocol,
-    OutputFormatterProtocol,
-    PostprocessorProtocol,
-    PreprocessorProtocol,
-    TargetProtocol,
-)
 
 
 class BatDetect2API:

src/batdetect2/audio/clips.py

@@ -6,13 +6,13 @@ from pydantic import Field
 from soundevent import data
 from soundevent.geometry import compute_bounds, intervals_overlap
 
+from batdetect2.audio.types import ClipperProtocol
 from batdetect2.core import (
     BaseConfig,
     ImportConfig,
     Registry,
     add_import_config,
 )
-from batdetect2.typing import ClipperProtocol
 
 DEFAULT_TRAIN_CLIP_DURATION = 0.256
 DEFAULT_MAX_EMPTY_CLIP = 0.1

src/batdetect2/audio/loader.py

@@ -5,8 +5,8 @@ from scipy.signal import resample, resample_poly
 from soundevent import audio, data
 from soundfile import LibsndfileError
 
+from batdetect2.audio.types import AudioLoader
 from batdetect2.core import BaseConfig
-from batdetect2.typing import AudioLoader
 
 __all__ = [
     "SoundEventAudioLoader",

src/batdetect2/audio/types.py

@@ -0,0 +1,40 @@
+from typing import Protocol
+
+import numpy as np
+from soundevent import data
+
+__all__ = [
+    "AudioLoader",
+    "ClipperProtocol",
+]
+
+
+class AudioLoader(Protocol):
+    samplerate: int
+
+    def load_file(
+        self,
+        path: data.PathLike,
+        audio_dir: data.PathLike | None = None,
+    ) -> np.ndarray: ...
+
+    def load_recording(
+        self,
+        recording: data.Recording,
+        audio_dir: data.PathLike | None = None,
+    ) -> np.ndarray: ...
+
+    def load_clip(
+        self,
+        clip: data.Clip,
+        audio_dir: data.PathLike | None = None,
+    ) -> np.ndarray: ...
+
+
+class ClipperProtocol(Protocol):
+    def __call__(
+        self,
+        clip_annotation: data.ClipAnnotation,
+    ) -> data.ClipAnnotation: ...
+
+    def get_subclip(self, clip: data.Clip) -> data.Clip: ...

src/batdetect2/core/registries.py

@@ -4,6 +4,7 @@ from typing import (
     Concatenate,
     Generic,
     ParamSpec,
+    Sequence,
     Type,
     TypeVar,
 )
@@ -147,6 +148,7 @@ T_Import = TypeVar("T_Import", bound=ImportConfig)
 
 def add_import_config(
     registry: Registry[T_Type, P_Type],
+    arg_names: Sequence[str] | None = None,
 ) -> Callable[[Type[T_Import]], Type[T_Import]]:
     """Decorator that registers an ImportConfig subclass as an escape hatch.
 
@@ -181,15 +183,22 @@ def add_import_config(
             *args: P_Type.args,
             **kwargs: P_Type.kwargs,
         ) -> T_Type:
-            if len(args) > 0:
+            _arg_names = arg_names or []
+
+            if len(args) != len(_arg_names):
                 raise ValueError(
                     "Positional arguments are not supported "
-                    "for import escape hatch."
+                    "for import escape hatch unless you specify "
+                    "the argument names. Use `arg_names` to specify "
+                    "the names of the positional arguments."
                 )
 
+            args_dict = {_arg_names[i]: args[i] for i in range(len(args))}
+
             hydra_cfg = {
                 "_target_": config.target,
                 **config.arguments,
+                **args_dict,
                 **kwargs,
             }
             return instantiate(hydra_cfg)

src/batdetect2/data/iterators.py

@@ -3,7 +3,7 @@ from collections.abc import Generator
 from soundevent import data
 
 from batdetect2.data.datasets import Dataset
-from batdetect2.typing.targets import TargetProtocol
+from batdetect2.targets.types import TargetProtocol
 
 
 def iterate_over_sound_events(

src/batdetect2/data/split.py

@@ -5,7 +5,7 @@ from batdetect2.data.summary import (
     extract_recordings_df,
     extract_sound_events_df,
 )
-from batdetect2.typing.targets import TargetProtocol
+from batdetect2.targets.types import TargetProtocol
 
 
 def split_dataset_by_recordings(

src/batdetect2/data/summary.py

@@ -2,7 +2,7 @@ import pandas as pd
 from soundevent.geometry import compute_bounds
 
 from batdetect2.data.datasets import Dataset
-from batdetect2.typing.targets import TargetProtocol
+from batdetect2.targets.types import TargetProtocol
 
 __all__ = [
     "extract_recordings_df",

src/batdetect2/evaluate/affinity.py

@@ -16,7 +16,8 @@ from batdetect2.core import (
     Registry,
     add_import_config,
 )
-from batdetect2.typing import AffinityFunction, Detection
+from batdetect2.evaluate.types import AffinityFunction
+from batdetect2.postprocess.types import Detection
 
 affinity_functions: Registry[AffinityFunction, []] = Registry(
     "affinity_function"

src/batdetect2/evaluate/dataset.py

@@ -8,14 +8,11 @@ from torch.utils.data import DataLoader, Dataset
 
 from batdetect2.audio import ClipConfig, build_audio_loader, build_clipper
 from batdetect2.audio.clips import PaddedClipConfig
+from batdetect2.audio.types import AudioLoader, ClipperProtocol
 from batdetect2.core import BaseConfig
 from batdetect2.core.arrays import adjust_width
 from batdetect2.preprocess import build_preprocessor
-from batdetect2.typing import (
+from batdetect2.preprocess.types import PreprocessorProtocol
-    AudioLoader,
-    ClipperProtocol,
-    PreprocessorProtocol,
-)
 
 __all__ = [
     "TestDataset",

src/batdetect2/evaluate/evaluate.py

@@ -5,22 +5,20 @@ from lightning import Trainer
 from soundevent import data
 
 from batdetect2.audio import build_audio_loader
+from batdetect2.audio.types import AudioLoader
 from batdetect2.evaluate.dataset import build_test_loader
 from batdetect2.evaluate.evaluator import build_evaluator
 from batdetect2.evaluate.lightning import EvaluationModule
 from batdetect2.logging import build_logger
 from batdetect2.models import Model
 from batdetect2.outputs import build_output_transform
-from batdetect2.typing import Detection
+from batdetect2.outputs.types import OutputFormatterProtocol
+from batdetect2.postprocess.types import Detection
+from batdetect2.preprocess.types import PreprocessorProtocol
+from batdetect2.targets.types import TargetProtocol
 
 if TYPE_CHECKING:
     from batdetect2.config import BatDetect2Config
-    from batdetect2.typing import (
-        AudioLoader,
-        OutputFormatterProtocol,
-        PreprocessorProtocol,
-        TargetProtocol,
-    )
 
 DEFAULT_EVAL_DIR: Path = Path("outputs") / "evaluations"
 

src/batdetect2/evaluate/evaluator.py

@@ -5,9 +5,10 @@ from soundevent import data
 
 from batdetect2.evaluate.config import EvaluationConfig
 from batdetect2.evaluate.tasks import build_task
+from batdetect2.evaluate.types import EvaluatorProtocol
+from batdetect2.postprocess.types import ClipDetections
 from batdetect2.targets import build_targets
-from batdetect2.typing import EvaluatorProtocol, TargetProtocol
+from batdetect2.targets.types import TargetProtocol
-from batdetect2.typing.postprocess import ClipDetections
 
 __all__ = [
     "Evaluator",

src/batdetect2/evaluate/lightning.py

@@ -5,12 +5,12 @@ from soundevent import data
 from torch.utils.data import DataLoader
 
 from batdetect2.evaluate.dataset import TestDataset, TestExample
+from batdetect2.evaluate.types import EvaluatorProtocol
 from batdetect2.logging import get_image_logger
 from batdetect2.models import Model
 from batdetect2.outputs import OutputTransformProtocol, build_output_transform
 from batdetect2.postprocess import to_raw_predictions
-from batdetect2.typing import EvaluatorProtocol
+from batdetect2.postprocess.types import ClipDetections
-from batdetect2.typing.postprocess import ClipDetections
 
 
 class EvaluationModule(LightningModule):

src/batdetect2/evaluate/metrics/classification.py

@@ -26,7 +26,8 @@ from batdetect2.evaluate.metrics.common import (
     average_precision,
     compute_precision_recall,
 )
-from batdetect2.typing import Detection, TargetProtocol
+from batdetect2.postprocess.types import Detection
+from batdetect2.targets.types import TargetProtocol
 
 __all__ = [
     "ClassificationMetric",

src/batdetect2/evaluate/metrics/detection.py

@@ -20,7 +20,7 @@ from batdetect2.core import (
     add_import_config,
 )
 from batdetect2.evaluate.metrics.common import average_precision
-from batdetect2.typing import Detection
+from batdetect2.postprocess.types import Detection
 
 __all__ = [
     "DetectionMetricConfig",

src/batdetect2/evaluate/metrics/top_class.py

@@ -20,8 +20,8 @@ from batdetect2.core import (
     add_import_config,
 )
 from batdetect2.evaluate.metrics.common import average_precision
-from batdetect2.typing import Detection
+from batdetect2.postprocess.types import Detection
-from batdetect2.typing.targets import TargetProtocol
+from batdetect2.targets.types import TargetProtocol
 
 __all__ = [
     "TopClassMetricConfig",

src/batdetect2/evaluate/plots/base.py

@@ -2,7 +2,7 @@ import matplotlib.pyplot as plt
 from matplotlib.figure import Figure
 
 from batdetect2.core import BaseConfig
-from batdetect2.typing import TargetProtocol
+from batdetect2.targets.types import TargetProtocol
 
 
 class BasePlotConfig(BaseConfig):

src/batdetect2/evaluate/plots/classification.py

@@ -29,7 +29,7 @@ from batdetect2.plotting.metrics import (
     plot_threshold_recall_curve,
     plot_threshold_recall_curves,
 )
-from batdetect2.typing import TargetProtocol
+from batdetect2.targets.types import TargetProtocol
 
 ClassificationPlotter = Callable[
     [Sequence[ClipEval]], Iterable[Tuple[str, Figure]]

src/batdetect2/evaluate/plots/clip_classification.py

@@ -22,7 +22,7 @@ from batdetect2.plotting.metrics import (
     plot_roc_curve,
     plot_roc_curves,
 )
-from batdetect2.typing import TargetProtocol
+from batdetect2.targets.types import TargetProtocol
 
 __all__ = [
     "ClipClassificationPlotConfig",

src/batdetect2/evaluate/plots/clip_detection.py

@@ -18,7 +18,7 @@ from batdetect2.evaluate.metrics.clip_detection import ClipEval
 from batdetect2.evaluate.metrics.common import compute_precision_recall
 from batdetect2.evaluate.plots.base import BasePlot, BasePlotConfig
 from batdetect2.plotting.metrics import plot_pr_curve, plot_roc_curve
-from batdetect2.typing import TargetProtocol
+from batdetect2.targets.types import TargetProtocol
 
 __all__ = [
     "ClipDetectionPlotConfig",

src/batdetect2/evaluate/plots/detection.py

@@ -16,6 +16,7 @@ from pydantic import Field
 from sklearn import metrics
 
 from batdetect2.audio import AudioConfig, build_audio_loader
+from batdetect2.audio.types import AudioLoader
 from batdetect2.core import ImportConfig, Registry, add_import_config
 from batdetect2.evaluate.metrics.common import compute_precision_recall
 from batdetect2.evaluate.metrics.detection import ClipEval
@@ -23,7 +24,8 @@ from batdetect2.evaluate.plots.base import BasePlot, BasePlotConfig
 from batdetect2.plotting.detections import plot_clip_detections
 from batdetect2.plotting.metrics import plot_pr_curve, plot_roc_curve
 from batdetect2.preprocess import PreprocessingConfig, build_preprocessor
-from batdetect2.typing import AudioLoader, PreprocessorProtocol, TargetProtocol
+from batdetect2.preprocess.types import PreprocessorProtocol
+from batdetect2.targets.types import TargetProtocol
 
 DetectionPlotter = Callable[[Sequence[ClipEval]], Iterable[Tuple[str, Figure]]]
 

src/batdetect2/evaluate/plots/top_class.py

@@ -16,6 +16,7 @@ from pydantic import Field
 from sklearn import metrics
 
 from batdetect2.audio import AudioConfig, build_audio_loader
+from batdetect2.audio.types import AudioLoader
 from batdetect2.core import ImportConfig, Registry, add_import_config
 from batdetect2.evaluate.metrics.common import compute_precision_recall
 from batdetect2.evaluate.metrics.top_class import (
@@ -27,7 +28,8 @@ from batdetect2.evaluate.plots.base import BasePlot, BasePlotConfig
 from batdetect2.plotting.gallery import plot_match_gallery
 from batdetect2.plotting.metrics import plot_pr_curve, plot_roc_curve
 from batdetect2.preprocess import PreprocessingConfig, build_preprocessor
-from batdetect2.typing import AudioLoader, PreprocessorProtocol, TargetProtocol
+from batdetect2.preprocess.types import PreprocessorProtocol
+from batdetect2.targets.types import TargetProtocol
 
 TopClassPlotter = Callable[[Sequence[ClipEval]], Iterable[tuple[str, Figure]]]
 

src/batdetect2/evaluate/tasks/__init__.py

@@ -11,12 +11,10 @@ from batdetect2.evaluate.tasks.clip_classification import (
 from batdetect2.evaluate.tasks.clip_detection import ClipDetectionTaskConfig
 from batdetect2.evaluate.tasks.detection import DetectionTaskConfig
 from batdetect2.evaluate.tasks.top_class import TopClassDetectionTaskConfig
+from batdetect2.evaluate.types import EvaluatorProtocol
+from batdetect2.postprocess.types import ClipDetections
 from batdetect2.targets import build_targets
-from batdetect2.typing import (
+from batdetect2.targets.types import TargetProtocol
-    ClipDetections,
-    EvaluatorProtocol,
-    TargetProtocol,
-)
 
 __all__ = [
     "TaskConfig",

src/batdetect2/evaluate/tasks/base.py

@@ -26,13 +26,12 @@ from batdetect2.evaluate.affinity import (
     TimeAffinityConfig,
     build_affinity_function,
 )
-from batdetect2.typing import (
+from batdetect2.evaluate.types import (
     AffinityFunction,
-    ClipDetections,
-    Detection,
     EvaluatorProtocol,
-    TargetProtocol,
 )
+from batdetect2.postprocess.types import ClipDetections, Detection
+from batdetect2.targets.types import TargetProtocol
 
 __all__ = [
     "BaseTaskConfig",

src/batdetect2/evaluate/tasks/classification.py

@@ -21,11 +21,8 @@ from batdetect2.evaluate.tasks.base import (
     BaseSEDTaskConfig,
     tasks_registry,
 )
-from batdetect2.typing import (
+from batdetect2.postprocess.types import ClipDetections, Detection
-    ClipDetections,
+from batdetect2.targets.types import TargetProtocol
-    Detection,
-    TargetProtocol,
-)
 
 
 class ClassificationTaskConfig(BaseSEDTaskConfig):

src/batdetect2/evaluate/tasks/clip_classification.py

@@ -19,7 +19,8 @@ from batdetect2.evaluate.tasks.base import (
     BaseTaskConfig,
     tasks_registry,
 )
-from batdetect2.typing import ClipDetections, TargetProtocol
+from batdetect2.postprocess.types import ClipDetections
+from batdetect2.targets.types import TargetProtocol
 
 
 class ClipClassificationTaskConfig(BaseTaskConfig):

src/batdetect2/evaluate/tasks/clip_detection.py

@@ -18,7 +18,8 @@ from batdetect2.evaluate.tasks.base import (
     BaseTaskConfig,
     tasks_registry,
 )
-from batdetect2.typing import ClipDetections, TargetProtocol
+from batdetect2.postprocess.types import ClipDetections
+from batdetect2.targets.types import TargetProtocol
 
 
 class ClipDetectionTaskConfig(BaseTaskConfig):

src/batdetect2/evaluate/tasks/detection.py

@@ -20,8 +20,8 @@ from batdetect2.evaluate.tasks.base import (
     BaseSEDTaskConfig,
     tasks_registry,
 )
-from batdetect2.typing import TargetProtocol
+from batdetect2.postprocess.types import ClipDetections
-from batdetect2.typing.postprocess import ClipDetections
+from batdetect2.targets.types import TargetProtocol
 
 
 class DetectionTaskConfig(BaseSEDTaskConfig):

src/batdetect2/evaluate/tasks/top_class.py

@@ -20,7 +20,8 @@ from batdetect2.evaluate.tasks.base import (
     BaseSEDTaskConfig,
     tasks_registry,
 )
-from batdetect2.typing import ClipDetections, TargetProtocol
+from batdetect2.postprocess.types import ClipDetections
+from batdetect2.targets.types import TargetProtocol
 
 
 class TopClassDetectionTaskConfig(BaseSEDTaskConfig):

src/batdetect2/evaluate/types.py

@@ -1,45 +1,39 @@
 from dataclasses import dataclass
-from typing import (
+from typing import Generic, Iterable, Protocol, Sequence, TypeVar
-    Generic,
-    Iterable,
-    Protocol,
-    Sequence,
-    TypeVar,
-)
 
 from matplotlib.figure import Figure
 from soundevent import data
 
-from batdetect2.typing.postprocess import ClipDetections, Detection
+from batdetect2.postprocess.types import ClipDetections, Detection
-from batdetect2.typing.targets import TargetProtocol
+from batdetect2.targets.types import TargetProtocol
 
 __all__ = [
+    "AffinityFunction",
+    "ClipMatches",
     "EvaluatorProtocol",
-    "MetricsProtocol",
     "MatchEvaluation",
+    "MatcherProtocol",
+    "MetricsProtocol",
+    "PlotterProtocol",
 ]
 
 
 @dataclass
 class MatchEvaluation:
     clip: data.Clip
-
     sound_event_annotation: data.SoundEventAnnotation | None
     gt_det: bool
     gt_class: str | None
     gt_geometry: data.Geometry | None
-
     pred_score: float
     pred_class_scores: dict[str, float]
     pred_geometry: data.Geometry | None
-
     affinity: float
 
     @property
     def top_class(self) -> str | None:
         if not self.pred_class_scores:
             return None
-
         return max(self.pred_class_scores, key=self.pred_class_scores.get)  # type: ignore
 
     @property
@@ -53,10 +47,8 @@ class MatchEvaluation:
     @property
     def top_class_score(self) -> float:
         pred_class = self.top_class
-
         if pred_class is None:
             return 0
-
         return self.pred_class_scores[pred_class]
 
 
@@ -75,9 +67,6 @@ class MatcherProtocol(Protocol):
     ) -> Iterable[tuple[int | None, int | None, float]]: ...
 
 
-Geom = TypeVar("Geom", bound=data.Geometry, contravariant=True)
-
-
 class AffinityFunction(Protocol):
     def __call__(
         self,
@@ -115,9 +104,11 @@ class EvaluatorProtocol(Protocol, Generic[EvaluationOutput]):
     ) -> EvaluationOutput: ...
 
     def compute_metrics(
-        self, eval_outputs: EvaluationOutput
+        self,
+        eval_outputs: EvaluationOutput,
     ) -> dict[str, float]: ...
 
     def generate_plots(
-        self, eval_outputs: EvaluationOutput
+        self,
+        eval_outputs: EvaluationOutput,
     ) -> Iterable[tuple[str, Figure]]: ...

src/batdetect2/inference/batch.py

@@ -4,22 +4,20 @@ from lightning import Trainer
 from soundevent import data
 
 from batdetect2.audio.loader import build_audio_loader
+from batdetect2.audio.types import AudioLoader
 from batdetect2.inference.clips import get_clips_from_files
 from batdetect2.inference.dataset import build_inference_loader
 from batdetect2.inference.lightning import InferenceModule
 from batdetect2.models import Model
 from batdetect2.outputs import OutputTransformProtocol, build_output_transform
+from batdetect2.postprocess.types import ClipDetections
 from batdetect2.preprocess.preprocessor import build_preprocessor
+from batdetect2.preprocess.types import PreprocessorProtocol
 from batdetect2.targets.targets import build_targets
-from batdetect2.typing.postprocess import ClipDetections
+from batdetect2.targets.types import TargetProtocol
 
 if TYPE_CHECKING:
     from batdetect2.config import BatDetect2Config
-    from batdetect2.typing import (
-        AudioLoader,
-        PreprocessorProtocol,
-        TargetProtocol,
-    )
 
 
 def run_batch_inference(

src/batdetect2/inference/dataset.py

@@ -6,10 +6,11 @@ from soundevent import data
 from torch.utils.data import DataLoader, Dataset
 
 from batdetect2.audio import build_audio_loader
+from batdetect2.audio.types import AudioLoader
 from batdetect2.core import BaseConfig
 from batdetect2.core.arrays import adjust_width
 from batdetect2.preprocess import build_preprocessor
-from batdetect2.typing import AudioLoader, PreprocessorProtocol
+from batdetect2.preprocess.types import PreprocessorProtocol
 
 __all__ = [
     "InferenceDataset",

src/batdetect2/inference/lightning.py

@@ -7,7 +7,7 @@ from batdetect2.inference.dataset import DatasetItem, InferenceDataset
 from batdetect2.models import Model
 from batdetect2.outputs import OutputTransformProtocol, build_output_transform
 from batdetect2.postprocess import to_raw_predictions
-from batdetect2.typing.postprocess import ClipDetections
+from batdetect2.postprocess.types import ClipDetections
 
 
 class InferenceModule(LightningModule):

src/batdetect2/models/__init__.py

@@ -62,16 +62,16 @@ from batdetect2.models.encoder import (
     build_encoder,
 )
 from batdetect2.models.heads import BBoxHead, ClassifierHead, DetectorHead
+from batdetect2.models.types import DetectionModel
 from batdetect2.postprocess.config import PostprocessConfig
-from batdetect2.preprocess.config import PreprocessingConfig
+from batdetect2.postprocess.types import (
-from batdetect2.targets.config import TargetConfig
-from batdetect2.typing import (
     ClipDetectionsTensor,
-    DetectionModel,
     PostprocessorProtocol,
-    PreprocessorProtocol,
-    TargetProtocol,
 )
+from batdetect2.preprocess.config import PreprocessingConfig
+from batdetect2.preprocess.types import PreprocessorProtocol
+from batdetect2.targets.config import TargetConfig
+from batdetect2.targets.types import TargetProtocol
 
 __all__ = [
     "BBoxHead",

src/batdetect2/models/backbones.py

@@ -51,7 +51,7 @@ from batdetect2.models.encoder import (
     EncoderConfig,
     build_encoder,
 )
-from batdetect2.typing.models import (
+from batdetect2.models.types import (
     BackboneModel,
     BottleneckProtocol,
     DecoderProtocol,

src/batdetect2/models/bottleneck.py

@@ -31,7 +31,7 @@ from batdetect2.models.blocks import (
     VerticalConv,
     build_layer,
 )
-from batdetect2.typing.models import BottleneckProtocol
+from batdetect2.models.types import BottleneckProtocol
 
 __all__ = [
     "BottleneckConfig",

src/batdetect2/models/detectors.py

@@ -26,7 +26,7 @@ from batdetect2.models.backbones import (
     build_backbone,
 )
 from batdetect2.models.heads import BBoxHead, ClassifierHead
-from batdetect2.typing.models import BackboneModel, DetectionModel, ModelOutput
+from batdetect2.models.types import BackboneModel, DetectionModel, ModelOutput
 
 __all__ = [
     "Detector",

src/batdetect2/models/types.py

@@ -0,0 +1,86 @@
+from abc import ABC, abstractmethod
+from typing import NamedTuple, Protocol
+
+import torch
+
+__all__ = [
+    "BackboneModel",
+    "BlockProtocol",
+    "BottleneckProtocol",
+    "DecoderProtocol",
+    "DetectionModel",
+    "EncoderDecoderModel",
+    "EncoderProtocol",
+    "ModelOutput",
+]
+
+
+class BlockProtocol(Protocol):
+    in_channels: int
+    out_channels: int
+
+    def __call__(self, x: torch.Tensor) -> torch.Tensor: ...
+
+    def get_output_height(self, input_height: int) -> int: ...
+
+
+class EncoderProtocol(Protocol):
+    in_channels: int
+    out_channels: int
+    input_height: int
+    output_height: int
+
+    def __call__(self, x: torch.Tensor) -> list[torch.Tensor]: ...
+
+
+class BottleneckProtocol(Protocol):
+    in_channels: int
+    out_channels: int
+    input_height: int
+
+    def __call__(self, x: torch.Tensor) -> torch.Tensor: ...
+
+
+class DecoderProtocol(Protocol):
+    in_channels: int
+    out_channels: int
+    input_height: int
+    output_height: int
+    depth: int
+
+    def __call__(
+        self,
+        x: torch.Tensor,
+        residuals: list[torch.Tensor],
+    ) -> torch.Tensor: ...
+
+
+class ModelOutput(NamedTuple):
+    detection_probs: torch.Tensor
+    size_preds: torch.Tensor
+    class_probs: torch.Tensor
+    features: torch.Tensor
+
+
+class BackboneModel(ABC, torch.nn.Module):
+    input_height: int
+    out_channels: int
+
+    @abstractmethod
+    def forward(self, spec: torch.Tensor) -> torch.Tensor:
+        raise NotImplementedError
+
+
+class EncoderDecoderModel(BackboneModel):
+    bottleneck_channels: int
+
+    @abstractmethod
+    def encode(self, spec: torch.Tensor) -> torch.Tensor: ...
+
+    @abstractmethod
+    def decode(self, encoded: torch.Tensor) -> torch.Tensor: ...
+
+
+class DetectionModel(ABC, torch.nn.Module):
+    @abstractmethod
+    def forward(self, spec: torch.Tensor) -> ModelOutput: ...

src/batdetect2/outputs/formats/__init__.py

@@ -11,7 +11,7 @@ from batdetect2.outputs.formats.batdetect2 import BatDetect2OutputConfig
 from batdetect2.outputs.formats.parquet import ParquetOutputConfig
 from batdetect2.outputs.formats.raw import RawOutputConfig
 from batdetect2.outputs.formats.soundevent import SoundEventOutputConfig
-from batdetect2.typing import TargetProtocol
+from batdetect2.targets.types import TargetProtocol
 
 __all__ = [
     "BatDetect2OutputConfig",

src/batdetect2/outputs/formats/base.py

@@ -4,10 +4,8 @@ from typing import Literal
 from soundevent.data import PathLike
 
 from batdetect2.core import ImportConfig, Registry, add_import_config
-from batdetect2.typing import (
+from batdetect2.outputs.types import OutputFormatterProtocol
-    OutputFormatterProtocol,
+from batdetect2.targets.types import TargetProtocol
-    TargetProtocol,
-)
 
 __all__ = [
     "OutputFormatterProtocol",

src/batdetect2/outputs/formats/batdetect2.py

@@ -12,12 +12,9 @@ from batdetect2.outputs.formats.base import (
     output_formatters,
 )
 from batdetect2.targets import terms
-from batdetect2.typing import (
+from batdetect2.outputs.types import OutputFormatterProtocol
-    ClipDetections,
+from batdetect2.postprocess.types import ClipDetections, Detection
-    Detection,
+from batdetect2.targets.types import TargetProtocol
-    OutputFormatterProtocol,
-    TargetProtocol,
-)
 
 try:
     from typing import NotRequired  # type: ignore

src/batdetect2/outputs/formats/parquet.py

@@ -13,12 +13,9 @@ from batdetect2.outputs.formats.base import (
     make_path_relative,
     output_formatters,
 )
-from batdetect2.typing import (
+from batdetect2.outputs.types import OutputFormatterProtocol
-    ClipDetections,
+from batdetect2.postprocess.types import ClipDetections, Detection
-    Detection,
+from batdetect2.targets.types import TargetProtocol
-    OutputFormatterProtocol,
-    TargetProtocol,
-)
 
 
 class ParquetOutputConfig(BaseConfig):

src/batdetect2/outputs/formats/raw.py

@@ -14,12 +14,9 @@ from batdetect2.outputs.formats.base import (
     make_path_relative,
     output_formatters,
 )
-from batdetect2.typing import (
+from batdetect2.outputs.types import OutputFormatterProtocol
-    ClipDetections,
+from batdetect2.postprocess.types import ClipDetections, Detection
-    Detection,
+from batdetect2.targets.types import TargetProtocol
-    OutputFormatterProtocol,
-    TargetProtocol,
-)
 
 
 class RawOutputConfig(BaseConfig):

src/batdetect2/outputs/formats/soundevent.py

@@ -8,12 +8,9 @@ from batdetect2.core import BaseConfig
 from batdetect2.outputs.formats.base import (
     output_formatters,
 )
-from batdetect2.typing import (
+from batdetect2.outputs.types import OutputFormatterProtocol
-    ClipDetections,
+from batdetect2.postprocess.types import ClipDetections, Detection
-    Detection,
+from batdetect2.targets.types import TargetProtocol
-    OutputFormatterProtocol,
-    TargetProtocol,
-)
 
 
 class SoundEventOutputConfig(BaseConfig):

src/batdetect2/outputs/transforms.py

@@ -5,7 +5,7 @@ from typing import Protocol
 from soundevent.geometry import shift_geometry
 
 from batdetect2.core.configs import BaseConfig
-from batdetect2.typing import ClipDetections, Detection
+from batdetect2.postprocess.types import ClipDetections, Detection
 
 __all__ = [
     "OutputTransform",

src/batdetect2/outputs/types.py

@@ -1,8 +1,9 @@
-from typing import Generic, List, Protocol, Sequence, TypeVar
+from collections.abc import Sequence
+from typing import Generic, Protocol, TypeVar
 
 from soundevent.data import PathLike
 
-from batdetect2.typing.postprocess import ClipDetections
+from batdetect2.postprocess.types import ClipDetections
 
 __all__ = [
     "OutputFormatterProtocol",
@@ -12,7 +13,7 @@ T = TypeVar("T")
 
 
 class OutputFormatterProtocol(Protocol, Generic[T]):
-    def format(self, predictions: Sequence[ClipDetections]) -> List[T]: ...
+    def format(self, predictions: Sequence[ClipDetections]) -> list[T]: ...
 
     def save(
         self,
@@ -21,4 +22,4 @@ class OutputFormatterProtocol(Protocol, Generic[T]):
         audio_dir: PathLike | None = None,
     ) -> None: ...
 
-    def load(self, path: PathLike) -> List[T]: ...
+    def load(self, path: PathLike) -> list[T]: ...

src/batdetect2/plotting/clip_annotations.py

@@ -3,8 +3,8 @@ from soundevent import data, plot
 
 from batdetect2.plotting.clips import plot_clip
 from batdetect2.plotting.common import create_ax
-from batdetect2.typing.preprocess import PreprocessorProtocol
+from batdetect2.preprocess.types import PreprocessorProtocol
-from batdetect2.typing.targets import TargetProtocol
+from batdetect2.targets.types import TargetProtocol
 
 __all__ = [
     "plot_clip_annotation",

src/batdetect2/plotting/clip_predictions.py

@@ -8,7 +8,7 @@ from soundevent.plot.geometries import plot_geometry
 from soundevent.plot.tags import TagColorMapper, add_tags_legend, plot_tag
 
 from batdetect2.plotting.clips import plot_clip
-from batdetect2.typing.preprocess import PreprocessorProtocol
+from batdetect2.preprocess.types import PreprocessorProtocol
 
 __all__ = [
     "plot_clip_prediction",

src/batdetect2/plotting/clips.py

@@ -4,9 +4,10 @@ from matplotlib.axes import Axes
 from soundevent import data
 
 from batdetect2.audio import build_audio_loader
+from batdetect2.audio.types import AudioLoader
 from batdetect2.plotting.common import plot_spectrogram
 from batdetect2.preprocess import build_preprocessor
-from batdetect2.typing import AudioLoader, PreprocessorProtocol
+from batdetect2.preprocess.types import PreprocessorProtocol
 
 __all__ = [
     "plot_clip",

src/batdetect2/plotting/gallery.py

@@ -3,6 +3,7 @@ from typing import Sequence
 import matplotlib.pyplot as plt
 from matplotlib.figure import Figure
 
+from batdetect2.audio.types import AudioLoader
 from batdetect2.plotting.matches import (
     MatchProtocol,
     plot_cross_trigger_match,
@@ -10,7 +11,7 @@ from batdetect2.plotting.matches import (
     plot_false_positive_match,
     plot_true_positive_match,
 )
-from batdetect2.typing.preprocess import AudioLoader, PreprocessorProtocol
+from batdetect2.preprocess.types import PreprocessorProtocol
 
 __all__ = ["plot_match_gallery"]
 

src/batdetect2/plotting/matches.py

@@ -4,12 +4,10 @@ from matplotlib.axes import Axes
 from soundevent import data, plot
 from soundevent.geometry import compute_bounds
 
+from batdetect2.audio.types import AudioLoader
 from batdetect2.plotting.clips import plot_clip
-from batdetect2.typing import (
+from batdetect2.postprocess.types import Detection
-    AudioLoader,
+from batdetect2.preprocess.types import PreprocessorProtocol
-    Detection,
-    PreprocessorProtocol,
-)
 
 __all__ = [
     "plot_false_positive_match",

src/batdetect2/postprocess/clips.py

@@ -1,4 +1,4 @@
-from batdetect2.typing import ClipDetections
+from batdetect2.postprocess.types import ClipDetections
 
 
 class ClipTransform:

src/batdetect2/postprocess/decoding.py

@@ -5,11 +5,11 @@ from typing import List
 import numpy as np
 from soundevent import data
 
-from batdetect2.typing.postprocess import (
+from batdetect2.postprocess.types import (
     ClipDetectionsArray,
     Detection,
 )
-from batdetect2.typing.targets import TargetProtocol
+from batdetect2.targets.types import TargetProtocol
 
 __all__ = [
     "to_raw_predictions",

src/batdetect2/postprocess/extraction.py

@@ -19,7 +19,7 @@ from typing import List
 
 import torch
 
-from batdetect2.typing.postprocess import ClipDetectionsTensor
+from batdetect2.postprocess.types import ClipDetectionsTensor
 
 __all__ = [
     "extract_detection_peaks",

src/batdetect2/postprocess/postprocessor.py

@@ -1,18 +1,18 @@
 import torch
 from loguru import logger
 
+from batdetect2.models.types import ModelOutput
 from batdetect2.postprocess.config import (
     PostprocessConfig,
 )
 from batdetect2.postprocess.extraction import extract_detection_peaks
 from batdetect2.postprocess.nms import NMS_KERNEL_SIZE, non_max_suppression
 from batdetect2.postprocess.remapping import map_detection_to_clip
-from batdetect2.typing import ModelOutput
+from batdetect2.postprocess.types import (
-from batdetect2.typing.postprocess import (
     ClipDetectionsTensor,
     PostprocessorProtocol,
 )
-from batdetect2.typing.preprocess import PreprocessorProtocol
+from batdetect2.preprocess.types import PreprocessorProtocol
 
 __all__ = [
     "build_postprocessor",

src/batdetect2/postprocess/remapping.py

@@ -19,8 +19,8 @@ import torch
 import xarray as xr
 from soundevent.arrays import Dimensions
 
+from batdetect2.postprocess.types import ClipDetectionsTensor
 from batdetect2.preprocess import MAX_FREQ, MIN_FREQ
-from batdetect2.typing.postprocess import ClipDetectionsTensor
 
 __all__ = [
     "features_to_xarray",

src/batdetect2/postprocess/types.py

@@ -0,0 +1,85 @@
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, NamedTuple, Protocol
+
+import numpy as np
+import torch
+from soundevent import data
+
+from batdetect2.targets.types import Position, Size
+
+if TYPE_CHECKING:
+    from batdetect2.models.types import ModelOutput
+
+__all__ = [
+    "ClipDetections",
+    "ClipDetectionsArray",
+    "ClipDetectionsTensor",
+    "ClipPrediction",
+    "Detection",
+    "GeometryDecoder",
+    "PostprocessorProtocol",
+]
+
+
+class GeometryDecoder(Protocol):
+    def __call__(
+        self,
+        position: Position,
+        size: Size,
+        class_name: str | None = None,
+    ) -> data.Geometry: ...
+
+
+@dataclass
+class Detection:
+    geometry: data.Geometry
+    detection_score: float
+    class_scores: np.ndarray
+    features: np.ndarray
+
+
+class ClipDetectionsArray(NamedTuple):
+    scores: np.ndarray
+    sizes: np.ndarray
+    class_scores: np.ndarray
+    times: np.ndarray
+    frequencies: np.ndarray
+    features: np.ndarray
+
+
+class ClipDetectionsTensor(NamedTuple):
+    scores: torch.Tensor
+    sizes: torch.Tensor
+    class_scores: torch.Tensor
+    times: torch.Tensor
+    frequencies: torch.Tensor
+    features: torch.Tensor
+
+    def numpy(self) -> ClipDetectionsArray:
+        return ClipDetectionsArray(
+            scores=self.scores.detach().cpu().numpy(),
+            sizes=self.sizes.detach().cpu().numpy(),
+            class_scores=self.class_scores.detach().cpu().numpy(),
+            times=self.times.detach().cpu().numpy(),
+            frequencies=self.frequencies.detach().cpu().numpy(),
+            features=self.features.detach().cpu().numpy(),
+        )
+
+
+@dataclass
+class ClipDetections:
+    clip: data.Clip
+    detections: list[Detection]
+
+
+@dataclass
+class ClipPrediction:
+    clip: data.Clip
+    detection_score: float
+    class_scores: np.ndarray
+
+
+class PostprocessorProtocol(Protocol):
+    def __call__(
+        self, output: "ModelOutput"
+    ) -> list[ClipDetectionsTensor]: ...

src/batdetect2/preprocess/preprocessor.py

@@ -1,7 +1,7 @@
 """Assembles the full batdetect2 preprocessing pipeline.
 
 This module defines :class:`Preprocessor`, the concrete implementation of
-:class:`~batdetect2.typing.PreprocessorProtocol`, and the
+:class:`~batdetect2.preprocess.types.PreprocessorProtocol`, and the
 :func:`build_preprocessor` factory function that constructs it from a
 :class:`~batdetect2.preprocess.config.PreprocessingConfig`.
 
@@ -33,7 +33,7 @@ from batdetect2.preprocess.spectrogram import (
     build_spectrogram_resizer,
     build_spectrogram_transform,
 )
-from batdetect2.typing import PreprocessorProtocol
+from batdetect2.preprocess.types import PreprocessorProtocol
 
 __all__ = [
     "Preprocessor",
@@ -42,7 +42,7 @@ __all__ = [
 
 
 class Preprocessor(torch.nn.Module, PreprocessorProtocol):
-    """Standard implementation of the :class:`~batdetect2.typing.PreprocessorProtocol`.
+    """Standard implementation of the :class:`~batdetect2.preprocess.types.PreprocessorProtocol`.
 
     Wraps all preprocessing stages as ``torch.nn.Module`` submodules so
     that parameters (e.g. PCEN filter coefficients) can be tracked and

src/batdetect2/preprocess/types.py

@@ -0,0 +1,31 @@
+from typing import Protocol
+
+import numpy as np
+import torch
+
+__all__ = [
+    "PreprocessorProtocol",
+    "SpectrogramBuilder",
+]
+
+
+class SpectrogramBuilder(Protocol):
+    def __call__(self, wav: torch.Tensor) -> torch.Tensor: ...
+
+
+class PreprocessorProtocol(Protocol):
+    max_freq: float
+    min_freq: float
+    input_samplerate: int
+    output_samplerate: float
+
+    def __call__(self, wav: torch.Tensor) -> torch.Tensor: ...
+
+    def generate_spectrogram(self, wav: torch.Tensor) -> torch.Tensor: ...
+
+    def process_audio(self, wav: torch.Tensor) -> torch.Tensor: ...
+
+    def process_spectrogram(self, spec: torch.Tensor) -> torch.Tensor: ...
+
+    def process_numpy(self, wav: np.ndarray) -> np.ndarray:
+        return self(torch.tensor(wav)).numpy()

src/batdetect2/targets/classes.py

@@ -16,7 +16,7 @@ from batdetect2.data.conditions import (
 )
 from batdetect2.targets.rois import ROIMapperConfig
 from batdetect2.targets.terms import call_type, generic_class
-from batdetect2.typing.targets import SoundEventDecoder, SoundEventEncoder
+from batdetect2.targets.types import SoundEventDecoder, SoundEventEncoder
 
 __all__ = [
     "build_sound_event_decoder",

src/batdetect2/targets/rois.py

@@ -27,17 +27,13 @@ from pydantic import Field
 from soundevent import data
 
 from batdetect2.audio import AudioConfig, build_audio_loader
+from batdetect2.audio.types import AudioLoader
 from batdetect2.core import ImportConfig, Registry, add_import_config
 from batdetect2.core.arrays import spec_to_xarray
 from batdetect2.core.configs import BaseConfig
 from batdetect2.preprocess import PreprocessingConfig, build_preprocessor
-from batdetect2.typing import (
+from batdetect2.preprocess.types import PreprocessorProtocol
-    AudioLoader,
+from batdetect2.targets.types import Position, ROITargetMapper, Size
-    Position,
-    PreprocessorProtocol,
-    ROITargetMapper,
-    Size,
-)
 
 __all__ = [
     "Anchor",

src/batdetect2/targets/targets.py

@@ -16,7 +16,7 @@ from batdetect2.targets.rois import (
     AnchorBBoxMapperConfig,
     build_roi_mapper,
 )
-from batdetect2.typing.targets import Position, Size, TargetProtocol
+from batdetect2.targets.types import Position, Size, TargetProtocol
 
 
 class Targets(TargetProtocol):

src/batdetect2/targets/types.py

@@ -0,0 +1,60 @@
+from collections.abc import Callable
+from typing import Protocol
+
+import numpy as np
+from soundevent import data
+
+__all__ = [
+    "Position",
+    "ROITargetMapper",
+    "Size",
+    "SoundEventDecoder",
+    "SoundEventEncoder",
+    "SoundEventFilter",
+    "TargetProtocol",
+]
+
+SoundEventEncoder = Callable[[data.SoundEventAnnotation], str | None]
+SoundEventDecoder = Callable[[str], list[data.Tag]]
+SoundEventFilter = Callable[[data.SoundEventAnnotation], bool]
+
+Position = tuple[float, float]
+Size = np.ndarray
+
+
+class TargetProtocol(Protocol):
+    class_names: list[str]
+    detection_class_tags: list[data.Tag]
+    detection_class_name: str
+    dimension_names: list[str]
+
+    def filter(self, sound_event: data.SoundEventAnnotation) -> bool: ...
+
+    def encode_class(
+        self,
+        sound_event: data.SoundEventAnnotation,
+    ) -> str | None: ...
+
+    def decode_class(self, class_label: str) -> list[data.Tag]: ...
+
+    def encode_roi(
+        self,
+        sound_event: data.SoundEventAnnotation,
+    ) -> tuple[Position, Size]: ...
+
+    def decode_roi(
+        self,
+        position: Position,
+        size: Size,
+        class_name: str | None = None,
+    ) -> data.Geometry: ...
+
+
+class ROITargetMapper(Protocol):
+    dimension_names: list[str]
+
+    def encode(
+        self, sound_event: data.SoundEvent
+    ) -> tuple[Position, Size]: ...
+
+    def decode(self, position: Position, size: Size) -> data.Geometry: ...

src/batdetect2/train/augmentations.py

@@ -13,6 +13,7 @@ from soundevent.geometry import scale_geometry, shift_geometry
 
 from batdetect2.audio.clips import get_subclip_annotation
 from batdetect2.audio.loader import TARGET_SAMPLERATE_HZ
+from batdetect2.audio.types import AudioLoader
 from batdetect2.core.arrays import adjust_width
 from batdetect2.core.configs import BaseConfig, load_config
 from batdetect2.core.registries import (
@@ -20,7 +21,7 @@ from batdetect2.core.registries import (
     Registry,
     add_import_config,
 )
-from batdetect2.typing import AudioLoader, Augmentation
+from batdetect2.train.types import Augmentation
 
 __all__ = [
     "AugmentationConfig",

src/batdetect2/train/callbacks.py

@@ -5,17 +5,15 @@ from lightning.pytorch.callbacks import Callback
 from soundevent import data
 from torch.utils.data import DataLoader
 
+from batdetect2.evaluate.types import EvaluatorProtocol
 from batdetect2.logging import get_image_logger
+from batdetect2.models.types import ModelOutput
 from batdetect2.outputs import OutputTransformProtocol, build_output_transform
 from batdetect2.postprocess import to_raw_predictions
+from batdetect2.postprocess.types import ClipDetections
 from batdetect2.train.dataset import ValidationDataset
 from batdetect2.train.lightning import TrainingModule
-from batdetect2.typing import (
+from batdetect2.train.types import TrainExample
-    ClipDetections,
-    EvaluatorProtocol,
-    ModelOutput,
-    TrainExample,
-)
 
 
 class ValidationMetrics(Callback):

src/batdetect2/train/dataset.py

@@ -8,9 +8,11 @@ from torch.utils.data import DataLoader, Dataset
 
 from batdetect2.audio import ClipConfig, build_audio_loader, build_clipper
 from batdetect2.audio.clips import PaddedClipConfig
+from batdetect2.audio.types import AudioLoader, ClipperProtocol
 from batdetect2.core import BaseConfig
 from batdetect2.core.arrays import adjust_width
 from batdetect2.preprocess import build_preprocessor
+from batdetect2.preprocess.types import PreprocessorProtocol
 from batdetect2.train.augmentations import (
     DEFAULT_AUGMENTATION_CONFIG,
     AugmentationsConfig,
@@ -18,14 +20,7 @@ from batdetect2.train.augmentations import (
     build_augmentations,
 )
 from batdetect2.train.labels import build_clip_labeler
-from batdetect2.typing import (
+from batdetect2.train.types import Augmentation, ClipLabeller, TrainExample
-    AudioLoader,
-    Augmentation,
-    ClipLabeller,
-    ClipperProtocol,
-    PreprocessorProtocol,
-    TrainExample,
-)
 
 __all__ = [
     "TrainingDataset",

src/batdetect2/train/labels.py

@@ -15,7 +15,8 @@ from soundevent import data
 from batdetect2.core.configs import BaseConfig, load_config
 from batdetect2.preprocess import MAX_FREQ, MIN_FREQ
 from batdetect2.targets import build_targets, iterate_encoded_sound_events
-from batdetect2.typing import ClipLabeller, Heatmaps, TargetProtocol
+from batdetect2.targets.types import TargetProtocol
+from batdetect2.train.types import ClipLabeller, Heatmaps
 
 __all__ = [
     "LabelConfig",

src/batdetect2/train/lightning.py

@@ -2,11 +2,12 @@ import lightning as L
 from soundevent.data import PathLike
 
 from batdetect2.models import Model, ModelConfig, build_model
+from batdetect2.models.types import ModelOutput
 from batdetect2.train.config import TrainingConfig
 from batdetect2.train.losses import build_loss
 from batdetect2.train.optimizers import build_optimizer
 from batdetect2.train.schedulers import build_scheduler
-from batdetect2.typing import LossProtocol, ModelOutput, TrainExample
+from batdetect2.train.types import LossProtocol, TrainExample
 
 __all__ = [
     "TrainingModule",

src/batdetect2/train/losses.py

@@ -26,7 +26,8 @@ from pydantic import Field
 from torch import nn
 
 from batdetect2.core.configs import BaseConfig
-from batdetect2.typing import Losses, LossProtocol, ModelOutput, TrainExample
+from batdetect2.models.types import ModelOutput
+from batdetect2.train.types import Losses, LossProtocol, TrainExample
 
 __all__ = [
     "BBoxLoss",

src/batdetect2/train/optimizers.py

@@ -43,7 +43,7 @@ optimizer_registry: Registry[Optimizer, [Iterable[nn.Parameter]]] = Registry(
 )
 
 
-@add_import_config(optimizer_registry)
+@add_import_config(optimizer_registry, arg_names=["params"])
 class OptimizerImportConfig(ImportConfig):
     """Use any callable as an optimizer.
 
@@ -84,4 +84,4 @@ def build_optimizer(
         Optimizer configuration. Defaults to ``AdamOptimizerConfig``.
     """
     config = config or AdamOptimizerConfig()
-    return optimizer_registry.build(config, params=parameters)
+    return optimizer_registry.build(config, parameters)

src/batdetect2/train/schedulers.py

@@ -40,7 +40,7 @@ class CosineAnnealingSchedulerConfig(BaseConfig):
 scheduler_registry: Registry[LRScheduler, [Optimizer]] = Registry("scheduler")
 
 
-@add_import_config(scheduler_registry)
+@add_import_config(scheduler_registry, arg_names=["optimizer"])
 class SchedulerImportConfig(ImportConfig):
     """Use any callable as a scheduler.
 
@@ -78,4 +78,4 @@ def build_scheduler(
     """Build a scheduler from configuration."""
     config = config or CosineAnnealingSchedulerConfig()
 
-    return scheduler_registry.build(config, optimizer=optimizer)
+    return scheduler_registry.build(config, optimizer)

src/batdetect2/train/train.py

@@ -1,32 +1,28 @@
 from collections.abc import Sequence
 from pathlib import Path
-from typing import TYPE_CHECKING, Optional
+from typing import Optional
 
 from lightning import Trainer, seed_everything
 from loguru import logger
 from soundevent import data
 
 from batdetect2.audio import AudioConfig, build_audio_loader
+from batdetect2.audio.types import AudioLoader
 from batdetect2.evaluate import build_evaluator
+from batdetect2.evaluate.types import EvaluatorProtocol
 from batdetect2.logging import build_logger
 from batdetect2.models import ModelConfig
 from batdetect2.preprocess import build_preprocessor
+from batdetect2.preprocess.types import PreprocessorProtocol
 from batdetect2.targets import build_targets
+from batdetect2.targets.types import TargetProtocol
 from batdetect2.train import TrainingConfig
 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
-
+from batdetect2.train.types import ClipLabeller
-if TYPE_CHECKING:
-    from batdetect2.typing import (
-        AudioLoader,
-        ClipLabeller,
-        EvaluatorProtocol,
-        PreprocessorProtocol,
-        TargetProtocol,
-    )
 
 __all__ = [
     "build_trainer",

src/batdetect2/train/types.py

@@ -0,0 +1,70 @@
+from collections.abc import Callable
+from typing import TYPE_CHECKING, NamedTuple, Protocol
+
+import torch
+from soundevent import data
+
+if TYPE_CHECKING:
+    from batdetect2.models.types import ModelOutput
+
+__all__ = [
+    "Augmentation",
+    "ClipLabeller",
+    "Heatmaps",
+    "Losses",
+    "LossProtocol",
+    "TrainExample",
+]
+
+
+class Heatmaps(NamedTuple):
+    detection: torch.Tensor
+    classes: torch.Tensor
+    size: torch.Tensor
+
+
+class PreprocessedExample(NamedTuple):
+    audio: torch.Tensor
+    spectrogram: torch.Tensor
+    detection_heatmap: torch.Tensor
+    class_heatmap: torch.Tensor
+    size_heatmap: torch.Tensor
+
+    def copy(self):
+        return PreprocessedExample(
+            audio=self.audio.clone(),
+            spectrogram=self.spectrogram.clone(),
+            detection_heatmap=self.detection_heatmap.clone(),
+            size_heatmap=self.size_heatmap.clone(),
+            class_heatmap=self.class_heatmap.clone(),
+        )
+
+
+ClipLabeller = Callable[[data.ClipAnnotation, torch.Tensor], Heatmaps]
+
+
+Augmentation = Callable[
+    [torch.Tensor, data.ClipAnnotation],
+    tuple[torch.Tensor, data.ClipAnnotation],
+]
+
+
+class TrainExample(NamedTuple):
+    spec: torch.Tensor
+    detection_heatmap: torch.Tensor
+    class_heatmap: torch.Tensor
+    size_heatmap: torch.Tensor
+    idx: torch.Tensor
+    start_time: torch.Tensor
+    end_time: torch.Tensor
+
+
+class Losses(NamedTuple):
+    detection: torch.Tensor
+    size: torch.Tensor
+    classification: torch.Tensor
+    total: torch.Tensor
+
+
+class LossProtocol(Protocol):
+    def __call__(self, pred: "ModelOutput", gt: TrainExample) -> Losses: ...

src/batdetect2/types.py

@@ -1,18 +1,14 @@
 """Types used in the code base."""
 
-from typing import Any, NamedTuple, TypedDict
+import sys
+from typing import Any, NamedTuple, Protocol, TypedDict
 
 import numpy as np
 import torch
 
-try:
+if sys.version_info >= (3, 11):
-    from typing import Protocol
+    from typing import NotRequired
-except ImportError:
+else:
-    from typing_extensions import Protocol
-
-try:
-    from typing import NotRequired  # type: ignore
-except ImportError:
     from typing_extensions import NotRequired
 
 

src/batdetect2/typing/__init__.py

@@ -1,75 +0,0 @@
-from batdetect2.typing.data import OutputFormatterProtocol
-from batdetect2.typing.evaluate import (
-    AffinityFunction,
-    ClipMatches,
-    EvaluatorProtocol,
-    MatcherProtocol,
-    MatchEvaluation,
-    MetricsProtocol,
-    PlotterProtocol,
-)
-from batdetect2.typing.models import BackboneModel, DetectionModel, ModelOutput
-from batdetect2.typing.postprocess import (
-    ClipDetections,
-    ClipDetectionsTensor,
-    Detection,
-    GeometryDecoder,
-    PostprocessorProtocol,
-)
-from batdetect2.typing.preprocess import (
-    AudioLoader,
-    PreprocessorProtocol,
-)
-from batdetect2.typing.targets import (
-    Position,
-    ROITargetMapper,
-    Size,
-    SoundEventDecoder,
-    SoundEventEncoder,
-    SoundEventFilter,
-    TargetProtocol,
-)
-from batdetect2.typing.train import (
-    Augmentation,
-    ClipLabeller,
-    ClipperProtocol,
-    Heatmaps,
-    Losses,
-    LossProtocol,
-    TrainExample,
-)
-
-__all__ = [
-    "AffinityFunction",
-    "AudioLoader",
-    "Augmentation",
-    "BackboneModel",
-    "ClipDetections",
-    "ClipDetectionsTensor",
-    "ClipLabeller",
-    "ClipMatches",
-    "ClipperProtocol",
-    "DetectionModel",
-    "EvaluatorProtocol",
-    "GeometryDecoder",
-    "Heatmaps",
-    "LossProtocol",
-    "Losses",
-    "MatchEvaluation",
-    "MatcherProtocol",
-    "MetricsProtocol",
-    "ModelOutput",
-    "OutputFormatterProtocol",
-    "PlotterProtocol",
-    "Position",
-    "PostprocessorProtocol",
-    "PreprocessorProtocol",
-    "ROITargetMapper",
-    "Detection",
-    "Size",
-    "SoundEventDecoder",
-    "SoundEventEncoder",
-    "SoundEventFilter",
-    "TargetProtocol",
-    "TrainExample",
-]

src/batdetect2/typing/models.py

@@ -1,287 +0,0 @@
-"""Defines shared interfaces (ABCs) and data structures for models.
-
-This module centralizes the definitions of core data structures, like the
-standard model output container (`ModelOutput`), and establishes abstract base
-classes (ABCs) using `abc.ABC` and `torch.nn.Module`. These define contracts
-for fundamental model components, ensuring modularity and consistent
-interaction within the `batdetect2.models` package.
-
-Key components:
-- `ModelOutput`: Standard structure for outputs from detection models.
-- `BackboneModel`: Generic interface for any feature extraction backbone.
-- `EncoderDecoderModel`: Specialized interface for backbones with distinct
-  encoder-decoder stages (e.g., U-Net), providing access to intermediate
-  features.
-- `DetectionModel`: Interface for the complete end-to-end detection model.
-"""
-
-from abc import ABC, abstractmethod
-from typing import List, NamedTuple, Protocol
-
-import torch
-
-__all__ = [
-    "ModelOutput",
-    "BackboneModel",
-    "EncoderDecoderModel",
-    "DetectionModel",
-    "BlockProtocol",
-    "EncoderProtocol",
-    "BottleneckProtocol",
-    "DecoderProtocol",
-]
-
-
-class BlockProtocol(Protocol):
-    """Interface for blocks of network layers."""
-
-    in_channels: int
-    out_channels: int
-
-    def __call__(self, x: torch.Tensor) -> torch.Tensor:
-        """Forward pass of the block."""
-        ...
-
-    def get_output_height(self, input_height: int) -> int:
-        """Calculate the output height based on input height."""
-        ...
-
-
-class EncoderProtocol(Protocol):
-    """Interface for the downsampling path of a network."""
-
-    in_channels: int
-    out_channels: int
-    input_height: int
-    output_height: int
-
-    def __call__(self, x: torch.Tensor) -> List[torch.Tensor]:
-        """Forward pass must return intermediate tensors for skip connections."""
-        ...
-
-
-class BottleneckProtocol(Protocol):
-    """Interface for the middle part of a U-Net-like network."""
-
-    in_channels: int
-    out_channels: int
-    input_height: int
-
-    def __call__(self, x: torch.Tensor) -> torch.Tensor:
-        """Processes the features from the encoder."""
-        ...
-
-
-class DecoderProtocol(Protocol):
-    """Interface for the upsampling reconstruction path."""
-
-    in_channels: int
-    out_channels: int
-    input_height: int
-    output_height: int
-    depth: int
-
-    def __call__(
-        self,
-        x: torch.Tensor,
-        residuals: List[torch.Tensor],
-    ) -> torch.Tensor:
-        """Upsamples features while integrating skip connections."""
-        ...
-
-
-class ModelOutput(NamedTuple):
-    """Standard container for the outputs of a BatDetect2 detection model.
-
-    This structure groups the different prediction tensors produced by the
-    model for a batch of input spectrograms. All tensors typically share the
-    same spatial dimensions (height H, width W) corresponding to the model's
-    output resolution, and the same batch size (N).
-
-    Attributes
-    ----------
-    detection_probs : torch.Tensor
-        Tensor containing the probability of sound event presence at each
-        location in the output grid.
-        Shape: `(N, 1, H, W)`
-    size_preds : torch.Tensor
-        Tensor containing the predicted size dimensions
-        (e.g., width and height) for a potential bounding box at each location.
-        Shape: `(N, 2, H, W)` (Channel 0 typically width, Channel 1 height)
-    class_probs : torch.Tensor
-        Tensor containing the predicted probabilities (or logits, depending on
-        the final activation) for each target class at each location.
-        The number of channels corresponds to the number of specific classes
-        defined in the `Targets` configuration.
-        Shape: `(N, num_classes, H, W)`
-    features : torch.Tensor
-        Tensor containing features extracted by the model's backbone. These
-        might be used for downstream tasks or analysis. The number of channels
-        depends on the specific model architecture.
-        Shape: `(N, num_features, H, W)`
-    """
-
-    detection_probs: torch.Tensor
-    size_preds: torch.Tensor
-    class_probs: torch.Tensor
-    features: torch.Tensor
-
-
-class BackboneModel(ABC, torch.nn.Module):
-    """Abstract Base Class for generic feature extraction backbone models.
-
-    Defines the minimal interface for a feature extractor network within a
-    BatDetect2 model. Its primary role is to process an input spectrogram
-    tensor and produce a spatially rich feature map tensor, which is then
-    typically consumed by separate prediction heads (for detection,
-    classification, size).
-
-    This base class is agnostic to the specific internal architecture (e.g.,
-    it could be a simple CNN, a U-Net, a Transformer, etc.). Concrete
-    implementations must inherit from this class and `torch.nn.Module`,
-    implement the `forward` method, and define the required attributes.
-
-    Attributes
-    ----------
-    input_height : int
-        Expected height (number of frequency bins) of the input spectrogram
-        tensor that the backbone is designed to process.
-    out_channels : int
-        Number of channels in the final feature map tensor produced by the
-        backbone's `forward` method.
-    """
-
-    input_height: int
-    """Expected input spectrogram height (frequency bins)."""
-
-    out_channels: int
-    """Number of output channels in the final feature map."""
-
-    @abstractmethod
-    def forward(self, spec: torch.Tensor) -> torch.Tensor:
-        """Perform the forward pass to extract features from the spectrogram.
-
-        Parameters
-        ----------
-        spec : torch.Tensor
-            Input spectrogram tensor, typically with shape
-            `(batch_size, 1, frequency_bins, time_bins)`.
-            `frequency_bins` should match `self.input_height`.
-
-        Returns
-        -------
-        torch.Tensor
-            Output feature map tensor, typically with shape
-            `(batch_size, self.out_channels, output_height, output_width)`.
-            The spatial dimensions (`output_height`, `output_width`) depend
-            on the specific backbone architecture (e.g., they might match the
-            input or be downsampled).
-        """
-        raise NotImplementedError
-
-
-class EncoderDecoderModel(BackboneModel):
-    """Abstract Base Class for Encoder-Decoder style backbone models.
-
-    This class specializes `BackboneModel` for architectures that have distinct
-    encoder stages (downsampling path), a bottleneck, and decoder stages
-    (upsampling path).
-
-    It provides separate abstract methods for the `encode` and `decode` steps,
-    allowing access to the intermediate "bottleneck" features produced by the
-    encoder. This can be useful for tasks like transfer learning or specialized
-    analyses.
-
-    Attributes
-    ----------
-    input_height : int
-        (Inherited from BackboneModel) Expected input spectrogram height.
-    out_channels : int
-        (Inherited from BackboneModel) Number of output channels in the final
-        feature map produced by the decoder/forward pass.
-    bottleneck_channels : int
-        Number of channels in the feature map produced by the encoder at its
-        deepest point (the bottleneck), before the decoder starts.
-    """
-
-    bottleneck_channels: int
-    """Number of channels at the encoder's bottleneck."""
-
-    @abstractmethod
-    def encode(self, spec: torch.Tensor) -> torch.Tensor:
-        """Process the input spectrogram through the encoder part.
-
-        Takes the input spectrogram and passes it through the downsampling path
-        of the network up to the bottleneck layer.
-
-        Parameters
-        ----------
-        spec : torch.Tensor
-            Input spectrogram tensor, typically with shape
-            `(batch_size, 1, frequency_bins, time_bins)`.
-
-        Returns
-        -------
-        torch.Tensor
-            The encoded feature map from the bottleneck layer, typically with
-            shape `(batch_size, self.bottleneck_channels, bottleneck_height,
-            bottleneck_width)`. The spatial dimensions are usually downsampled
-            relative to the input.
-        """
-        ...
-
-    @abstractmethod
-    def decode(self, encoded: torch.Tensor) -> torch.Tensor:
-        """Process the bottleneck features through the decoder part.
-
-        Takes the encoded feature map from the bottleneck and passes it through
-        the upsampling path (potentially using skip connections from the
-        encoder) to produce the final output feature map.
-
-        Parameters
-        ----------
-        encoded : torch.Tensor
-            The bottleneck feature map tensor produced by the `encode` method.
-
-        Returns
-        -------
-        torch.Tensor
-            The final output feature map tensor, typically with shape
-            `(batch_size, self.out_channels, output_height, output_width)`.
-            This should match the output shape of the `forward` method.
-        """
-        ...
-
-
-class DetectionModel(ABC, torch.nn.Module):
-    """Abstract Base Class for complete BatDetect2 detection models.
-
-    Defines the interface for the overall model that takes an input spectrogram
-    and produces all necessary outputs for detection, classification, and size
-    prediction, packaged within a `ModelOutput` object.
-
-    Concrete implementations typically combine a `BackboneModel` for feature
-    extraction with specific prediction heads for each output type. They must
-    inherit from this class and `torch.nn.Module`, and implement the `forward`
-    method.
-    """
-
-    @abstractmethod
-    def forward(self, spec: torch.Tensor) -> ModelOutput:
-        """Perform the forward pass of the full detection model.
-
-        Processes the input spectrogram through the backbone and prediction
-        heads to generate all required output tensors.
-
-        Parameters
-        ----------
-        spec : torch.Tensor
-            Input spectrogram tensor, typically with shape
-            `(batch_size, 1, frequency_bins, time_bins)`.
-
-        Returns
-        -------
-        ModelOutput
-            A NamedTuple containing the prediction tensors: `detection_probs`,
-            `size_preds`, `class_probs`, and `features`.
-        """

src/batdetect2/typing/postprocess.py

@@ -1,104 +0,0 @@
-"""Defines shared interfaces and data structures for postprocessing.
-
-This module centralizes the Protocol definitions and common data structures
-used throughout the `batdetect2.postprocess` module.
-
-The main component is the `PostprocessorProtocol`, which outlines the standard
-interface for an object responsible for executing the entire postprocessing
-pipeline. This pipeline transforms raw neural network outputs into interpretable
-detections represented as `soundevent` objects. Using protocols ensures
-modularity and consistent interaction between different parts of the BatDetect2
-system that deal with model predictions.
-"""
-
-from dataclasses import dataclass
-from typing import List, NamedTuple, Protocol
-
-import numpy as np
-import torch
-from soundevent import data
-
-from batdetect2.typing.models import ModelOutput
-from batdetect2.typing.targets import Position, Size
-
-__all__ = [
-    "Detection",
-    "PostprocessorProtocol",
-    "GeometryDecoder",
-]
-
-
-# TODO: update the docstring
-class GeometryDecoder(Protocol):
-    """Type alias for a function that recovers geometry from position and size.
-
-    This callable takes:
-    1.  A position tuple `(time, frequency)`.
-    2.  A NumPy array of size dimensions (e.g., `[width, height]`).
-    3.  Optionally a class name of the highest scoring class. This is to accomodate
-        different ways of decoding geometry that depend on the predicted class.
-    It should return the reconstructed `soundevent.data.Geometry` (typically a
-    `BoundingBox`).
-    """
-
-    def __call__(
-        self, position: Position, size: Size, class_name: str | None = None
-    ) -> data.Geometry: ...
-
-
-@dataclass
-class Detection:
-    geometry: data.Geometry
-    detection_score: float
-    class_scores: np.ndarray
-    features: np.ndarray
-
-
-class ClipDetectionsArray(NamedTuple):
-    scores: np.ndarray
-    sizes: np.ndarray
-    class_scores: np.ndarray
-    times: np.ndarray
-    frequencies: np.ndarray
-    features: np.ndarray
-
-
-class ClipDetectionsTensor(NamedTuple):
-    scores: torch.Tensor
-    sizes: torch.Tensor
-    class_scores: torch.Tensor
-    times: torch.Tensor
-    frequencies: torch.Tensor
-    features: torch.Tensor
-
-    def numpy(self) -> ClipDetectionsArray:
-        return ClipDetectionsArray(
-            scores=self.scores.detach().cpu().numpy(),
-            sizes=self.sizes.detach().cpu().numpy(),
-            class_scores=self.class_scores.detach().cpu().numpy(),
-            times=self.times.detach().cpu().numpy(),
-            frequencies=self.frequencies.detach().cpu().numpy(),
-            features=self.features.detach().cpu().numpy(),
-        )
-
-
-@dataclass
-class ClipDetections:
-    clip: data.Clip
-    detections: List[Detection]
-
-
-@dataclass
-class ClipPrediction:
-    clip: data.Clip
-    detection_score: float
-    class_scores: np.ndarray
-
-
-class PostprocessorProtocol(Protocol):
-    """Protocol defining the interface for the full postprocessing pipeline."""
-
-    def __call__(
-        self,
-        output: ModelOutput,
-    ) -> List[ClipDetectionsTensor]: ...

src/batdetect2/typing/preprocess.py

@@ -1,168 +0,0 @@
-"""Defines common interfaces (Protocols) for preprocessing components.
-
-This module centralizes the Protocol definitions used throughout the
-`batdetect2.preprocess` package. Protocols define expected methods and
-signatures, allowing for flexible and interchangeable implementations of
-components like audio loaders and spectrogram builders.
-
-Using these protocols ensures that different parts of the preprocessing
-pipeline can interact consistently, regardless of the specific underlying
-implementation (e.g., different libraries or custom configurations).
-"""
-
-from typing import Protocol
-
-import numpy as np
-import torch
-from soundevent import data
-
-__all__ = [
-    "AudioLoader",
-    "SpectrogramBuilder",
-    "PreprocessorProtocol",
-]
-
-
-class AudioLoader(Protocol):
-    """Defines the interface for an audio loading and processing component.
-
-    An AudioLoader is responsible for retrieving audio data corresponding to
-    different soundevent objects (files, Recordings, Clips) and applying a
-    configured set of initial preprocessing steps. Adhering to this protocol
-    allows for different loading strategies or implementations.
-    """
-
-    samplerate: int
-
-    def load_file(
-        self,
-        path: data.PathLike,
-        audio_dir: data.PathLike | None = None,
-    ) -> np.ndarray:
-        """Load and preprocess audio directly from a file path.
-
-        Parameters
-        ----------
-        path : PathLike
-            Path to the audio file.
-        audio_dir : PathLike, optional
-            A directory prefix to prepend to the path if `path` is relative.
-
-        Raises
-        ------
-        FileNotFoundError
-            If the audio file cannot be found.
-        Exception
-            If the audio file cannot be loaded or processed.
-        """
-        ...
-
-    def load_recording(
-        self,
-        recording: data.Recording,
-        audio_dir: data.PathLike | None = None,
-    ) -> np.ndarray:
-        """Load and preprocess the entire audio for a Recording object.
-
-        Parameters
-        ----------
-        recording : data.Recording
-            The Recording object containing metadata about the audio file.
-        audio_dir : PathLike, optional
-            A directory where the audio file associated with the recording
-            can be found, especially if the path in the recording is relative.
-
-        Returns
-        -------
-        np.ndarray
-            The loaded and preprocessed audio waveform as a 1-D NumPy
-            array. Typically loads only the first channel.
-
-        Raises
-        ------
-        FileNotFoundError
-            If the audio file associated with the recording cannot be found.
-        Exception
-            If the audio file cannot be loaded or processed.
-        """
-        ...
-
-    def load_clip(
-        self,
-        clip: data.Clip,
-        audio_dir: data.PathLike | None = None,
-    ) -> np.ndarray:
-        """Load and preprocess the audio segment defined by a Clip object.
-
-        Parameters
-        ----------
-        clip : data.Clip
-            The Clip object specifying the recording and the start/end times
-            of the segment to load.
-        audio_dir : PathLike, optional
-            A directory where the audio file associated with the clip's
-            recording can be found.
-
-        Returns
-        -------
-        np.ndarray
-            The loaded and preprocessed audio waveform for the specified
-            clip duration as a 1-D NumPy array. Typically loads only the
-            first channel.
-
-        Raises
-        ------
-        FileNotFoundError
-            If the audio file associated with the clip cannot be found.
-        Exception
-            If the audio file cannot be loaded or processed.
-        """
-        ...
-
-
-class SpectrogramBuilder(Protocol):
-    """Defines the interface for a spectrogram generation component."""
-
-    def __call__(self, wav: torch.Tensor) -> torch.Tensor:
-        """Generate a spectrogram from an audio waveform."""
-        ...
-
-
-class PreprocessorProtocol(Protocol):
-    """Defines a high-level interface for the complete preprocessing pipeline."""
-
-    max_freq: float
-
-    min_freq: float
-
-    input_samplerate: int
-
-    output_samplerate: float
-
-    def __call__(self, wav: torch.Tensor) -> torch.Tensor: ...
-
-    def generate_spectrogram(self, wav: torch.Tensor) -> torch.Tensor: ...
-
-    def process_audio(self, wav: torch.Tensor) -> torch.Tensor: ...
-
-    def process_spectrogram(self, spec: torch.Tensor) -> torch.Tensor: ...
-
-    def process_numpy(self, wav: np.ndarray) -> np.ndarray:
-        """Run the full preprocessing pipeline on a NumPy waveform.
-
-        This default implementation converts the array to a
-        ``torch.Tensor``, calls :meth:`__call__`, and converts the
-        result back to a NumPy array. Concrete implementations may
-        override this for efficiency.
-
-        Parameters
-        ----------
-        wav : np.ndarray
-            Input waveform as a 1-D NumPy array.
-
-        Returns
-        -------
-        np.ndarray
-            Preprocessed spectrogram as a NumPy array.
-        """
-        return self(torch.tensor(wav)).numpy()

src/batdetect2/typing/targets.py

@@ -1,298 +0,0 @@
-"""Defines the core interface (Protocol) for the target definition pipeline.
-
-This module specifies the standard structure, attributes, and methods expected
-from an object that encapsulates the complete configured logic for processing
-sound event annotations within the `batdetect2.targets` system.
-
-The main component defined here is the `TargetProtocol`. This protocol acts as
-a contract for the entire target definition process, covering semantic aspects
-(filtering, tag transformation, class encoding/decoding) as well as geometric
-aspects (mapping regions of interest to target positions and sizes). It ensures
-that components responsible for these tasks can be interacted with consistently
-throughout BatDetect2.
-"""
-
-from collections.abc import Callable
-from typing import List, Protocol
-
-import numpy as np
-from soundevent import data
-
-__all__ = [
-    "TargetProtocol",
-    "SoundEventEncoder",
-    "SoundEventDecoder",
-    "SoundEventFilter",
-    "Position",
-    "Size",
-]
-
-SoundEventEncoder = Callable[[data.SoundEventAnnotation], str | None]
-"""Type alias for a sound event class encoder function.
-
-An encoder function takes a sound event annotation and returns the string name
-of the target class it belongs to, based on a predefined set of rules.
-If the annotation does not match any defined target class according to the
-rules, the function returns None.
-"""
-
-
-SoundEventDecoder = Callable[[str], List[data.Tag]]
-"""Type alias for a sound event class decoder function.
-
-A decoder function takes a class name string (as predicted by the model or
-assigned during encoding) and returns a list of `soundevent.data.Tag` objects
-that represent that class according to the configuration. This is used to
-translate model outputs back into meaningful annotations.
-"""
-
-SoundEventFilter = Callable[[data.SoundEventAnnotation], bool]
-"""Type alias for a filter function.
-
-A filter function accepts a soundevent.data.SoundEventAnnotation object
-and returns True if the annotation should be kept based on the filter's
-criteria, or False if it should be discarded.
-"""
-
-Position = tuple[float, float]
-"""A tuple representing (time, frequency) coordinates."""
-
-Size = np.ndarray
-"""A NumPy array representing the size dimensions of a target."""
-
-
-class TargetProtocol(Protocol):
-    """Protocol defining the interface for the target definition pipeline.
-
-    This protocol outlines the standard attributes and methods for an object
-    that encapsulates the complete, configured process for handling sound event
-    annotations (both tags and geometry). It defines how to:
-    - Select relevant annotations.
-    - Encode an annotation into a specific target class name.
-    - Decode a class name back into representative tags.
-    - Extract a target reference position from an annotation's geometry (ROI).
-    - Calculate target size dimensions from an annotation's geometry.
-    - Recover an approximate geometry (ROI) from a position and size
-      dimensions.
-
-    Implementations of this protocol bundle all configured logic for these
-    steps.
-
-    Attributes
-    ----------
-    class_names : List[str]
-        An ordered list of the unique names of the specific target classes
-        defined by the configuration.
-    generic_class_tags : List[data.Tag]
-        A list of `soundevent.data.Tag` objects representing the configured
-        generic class category (e.g., used when no specific class matches).
-    dimension_names : List[str]
-        A list containing the names of the size dimensions returned by
-        `get_size` and expected by `recover_roi` (e.g., ['width', 'height']).
-    """
-
-    class_names: List[str]
-    """Ordered list of unique names for the specific target classes."""
-
-    detection_class_tags: List[data.Tag]
-    """List of tags representing the detection category (unclassified)."""
-
-    detection_class_name: str
-
-    dimension_names: List[str]
-    """Names of the size dimensions (e.g., ['width', 'height'])."""
-
-    def filter(self, sound_event: data.SoundEventAnnotation) -> bool:
-        """Apply the filter to a sound event annotation.
-
-        Determines if the annotation should be included in further processing
-        and training based on the configured filtering rules.
-
-        Parameters
-        ----------
-        sound_event : data.SoundEventAnnotation
-            The annotation to filter.
-
-        Returns
-        -------
-        bool
-            True if the annotation should be kept (passes the filter),
-            False otherwise. Implementations should return True if no
-            filtering is configured.
-        """
-        ...
-
-    def encode_class(
-        self,
-        sound_event: data.SoundEventAnnotation,
-    ) -> str | None:
-        """Encode a sound event annotation to its target class name.
-
-        Parameters
-        ----------
-        sound_event : data.SoundEventAnnotation
-            The (potentially filtered and transformed) annotation to encode.
-
-        Returns
-        -------
-        str or None
-            The string name of the matched target class if the annotation
-            matches a specific class definition. Returns None if the annotation
-            does not match any specific class rule (indicating it may belong
-            to a generic category or should be handled differently downstream).
-        """
-        ...
-
-    def decode_class(self, class_label: str) -> List[data.Tag]:
-        """Decode a predicted class name back into representative tags.
-
-        Parameters
-        ----------
-        class_label : str
-            The class name string (e.g., predicted by a model) to decode.
-
-        Returns
-        -------
-        List[data.Tag]
-            The list of tags corresponding to the input class name according
-            to the configuration. May return an empty list or raise an error
-            for unmapped labels, depending on the implementation's configuration
-            (e.g., `raise_on_unmapped` flag during building).
-
-        Raises
-        ------
-        ValueError, KeyError
-            Implementations might raise an error if the `class_label` is not
-            found in the configured mapping and error raising is enabled.
-        """
-        ...
-
-    def encode_roi(
-        self, sound_event: data.SoundEventAnnotation
-    ) -> tuple[Position, Size]:
-        """Extract the target reference position from the annotation's geometry.
-
-        Calculates the `(time, frequency)` coordinate representing the primary
-        location of the sound event.
-
-        Parameters
-        ----------
-        sound_event : data.SoundEventAnnotation
-            The annotation containing the geometry (ROI) to process.
-
-        Returns
-        -------
-        Tuple[float, float]
-            The calculated reference position `(time, frequency)`.
-
-        Raises
-        ------
-        ValueError
-            If the annotation lacks geometry or if the position cannot be
-            calculated for the geometry type or configured reference point.
-        """
-        ...
-
-    # TODO: Update docstrings
-    def decode_roi(
-        self,
-        position: Position,
-        size: Size,
-        class_name: str | None = None,
-    ) -> data.Geometry:
-        """Recover the ROI geometry from a position and dimensions.
-
-        Performs the inverse mapping of `get_position` and `get_size`. It takes
-        a reference position `(time, frequency)` and an array of size
-        dimensions and reconstructs an approximate geometric representation.
-
-        Parameters
-        ----------
-        pos : Tuple[float, float]
-            The reference position `(time, frequency)`.
-        dims : np.ndarray
-            The NumPy array containing the dimensions (e.g., predicted
-            by the model), corresponding to the order in `dimension_names`.
-        class_name: str
-            class
-
-        Returns
-        -------
-        soundevent.data.Geometry
-            The reconstructed geometry.
-
-        Raises
-        ------
-        ValueError
-            If the number of provided `dims` does not match `dimension_names`,
-            if dimensions are invalid (e.g., negative after unscaling), or
-            if reconstruction fails based on the configured position type.
-        """
-        ...
-
-
-class ROITargetMapper(Protocol):
-    """Protocol defining the interface for ROI-to-target mapping.
-
-    Specifies the `encode` and `decode` methods required for converting a
-    `soundevent.data.SoundEvent` into a target representation (a reference
-    position and a size vector) and for recovering an approximate ROI from that
-    representation.
-
-    Attributes
-    ----------
-    dimension_names : List[str]
-        A list containing the names of the dimensions in the `Size` array
-        returned by `encode` and expected by `decode`.
-    """
-
-    dimension_names: List[str]
-
-    def encode(self, sound_event: data.SoundEvent) -> tuple[Position, Size]:
-        """Encode a SoundEvent's geometry into a position and size.
-
-        Parameters
-        ----------
-        sound_event : data.SoundEvent
-            The input sound event, which must have a geometry attribute.
-
-        Returns
-        -------
-        Tuple[Position, Size]
-            A tuple containing:
-            - The reference position as (time, frequency) coordinates.
-            - A NumPy array with the calculated size dimensions.
-
-        Raises
-        ------
-        ValueError
-            If the sound event does not have a geometry.
-        """
-        ...
-
-    def decode(self, position: Position, size: Size) -> data.Geometry:
-        """Decode a position and size back into a geometric ROI.
-
-        Performs the inverse mapping: takes a reference position and size
-        dimensions and reconstructs a geometric representation.
-
-        Parameters
-        ----------
-        position : Position
-            The reference position (time, frequency).
-        size : Size
-            NumPy array containing the size dimensions, matching the order
-            and meaning specified by `dimension_names`.
-
-        Returns
-        -------
-        soundevent.data.Geometry
-            The reconstructed geometry, typically a `BoundingBox`.
-
-        Raises
-        ------
-        ValueError
-            If the `size` array has an unexpected shape or if reconstruction
-            fails.
-        """
-        ...

src/batdetect2/typing/train.py

@@ -1,108 +0,0 @@
-from typing import Callable, NamedTuple, Protocol
-
-import torch
-from soundevent import data
-
-from batdetect2.typing.models import ModelOutput
-
-__all__ = [
-    "Augmentation",
-    "ClipLabeller",
-    "ClipperProtocol",
-    "Heatmaps",
-    "LossProtocol",
-    "Losses",
-    "TrainExample",
-]
-
-
-class Heatmaps(NamedTuple):
-    """Structure holding the generated heatmap targets."""
-
-    detection: torch.Tensor
-    classes: torch.Tensor
-    size: torch.Tensor
-
-
-class PreprocessedExample(NamedTuple):
-    audio: torch.Tensor
-    spectrogram: torch.Tensor
-    detection_heatmap: torch.Tensor
-    class_heatmap: torch.Tensor
-    size_heatmap: torch.Tensor
-
-    def copy(self):
-        return PreprocessedExample(
-            audio=self.audio.clone(),
-            spectrogram=self.spectrogram.clone(),
-            detection_heatmap=self.detection_heatmap.clone(),
-            size_heatmap=self.size_heatmap.clone(),
-            class_heatmap=self.class_heatmap.clone(),
-        )
-
-
-ClipLabeller = Callable[[data.ClipAnnotation, torch.Tensor], Heatmaps]
-"""Type alias for the final clip labelling function.
-
-This function takes the complete annotations for a clip and the corresponding
-spectrogram, applies all configured filtering, transformation, and encoding
-steps, and returns the final `Heatmaps` used for model training.
-"""
-
-
-Augmentation = Callable[
-    [torch.Tensor, data.ClipAnnotation],
-    tuple[torch.Tensor, data.ClipAnnotation],
-]
-
-
-class TrainExample(NamedTuple):
-    spec: torch.Tensor
-    detection_heatmap: torch.Tensor
-    class_heatmap: torch.Tensor
-    size_heatmap: torch.Tensor
-    idx: torch.Tensor
-    start_time: torch.Tensor
-    end_time: torch.Tensor
-
-
-class Losses(NamedTuple):
-    """Structure to hold the computed loss values.
-
-    Allows returning individual loss components along with the total weighted
-    loss for monitoring and analysis during training.
-
-    Attributes
-    ----------
-    detection : torch.Tensor
-        Scalar tensor representing the calculated detection loss component
-        (before weighting).
-    size : torch.Tensor
-        Scalar tensor representing the calculated size regression loss component
-        (before weighting).
-    classification : torch.Tensor
-        Scalar tensor representing the calculated classification loss component
-        (before weighting).
-    total : torch.Tensor
-        Scalar tensor representing the final combined loss, computed as the
-        weighted sum of the detection, size, and classification components.
-        This is the value typically used for backpropagation.
-    """
-
-    detection: torch.Tensor
-    size: torch.Tensor
-    classification: torch.Tensor
-    total: torch.Tensor
-
-
-class LossProtocol(Protocol):
-    def __call__(self, pred: ModelOutput, gt: TrainExample) -> Losses: ...
-
-
-class ClipperProtocol(Protocol):
-    def __call__(
-        self,
-        clip_annotation: data.ClipAnnotation,
-    ) -> data.ClipAnnotation: ...
-
-    def get_subclip(self, clip: data.Clip) -> data.Clip: ...

tests/conftest.py

@@ -11,23 +11,20 @@ from soundevent import data, terms
 
 from batdetect2.audio import build_audio_loader
 from batdetect2.audio.clips import build_clipper
+from batdetect2.audio.types import AudioLoader, ClipperProtocol
 from batdetect2.data import DatasetConfig, load_dataset
 from batdetect2.data.annotations.batdetect2 import BatDetect2FilesAnnotations
 from batdetect2.preprocess import build_preprocessor
+from batdetect2.preprocess.types import PreprocessorProtocol
 from batdetect2.targets import (
     TargetConfig,
     build_targets,
     call_type,
 )
 from batdetect2.targets.classes import TargetClassConfig
+from batdetect2.targets.types import TargetProtocol
 from batdetect2.train.labels import build_clip_labeler
-from batdetect2.typing import (
+from batdetect2.train.types import ClipLabeller
-    ClipLabeller,
-    PreprocessorProtocol,
-    TargetProtocol,
-)
-from batdetect2.typing.preprocess import AudioLoader
-from batdetect2.typing.train import ClipperProtocol
 
 
 @pytest.fixture

tests/test_data/test_predictions/test_parquet.py

@@ -9,11 +9,8 @@ from batdetect2.outputs.formats import (
     ParquetOutputConfig,
     build_output_formatter,
 )
-from batdetect2.typing import (
+from batdetect2.postprocess.types import ClipDetections, Detection
-    ClipDetections,
+from batdetect2.targets.types import TargetProtocol
-    Detection,
-    TargetProtocol,
-)
 
 
 @pytest.fixture

tests/test_data/test_predictions/test_raw.py

@@ -5,11 +5,8 @@ import pytest
 from soundevent import data
 
 from batdetect2.outputs.formats import RawOutputConfig, build_output_formatter
-from batdetect2.typing import (
+from batdetect2.postprocess.types import ClipDetections, Detection
-    ClipDetections,
+from batdetect2.targets.types import TargetProtocol
-    Detection,
-    TargetProtocol,
-)
 
 
 @pytest.fixture

tests/test_evaluate/test_tasks/conftest.py

@@ -4,7 +4,7 @@ import numpy as np
 import pytest
 from soundevent import data
 
-from batdetect2.typing import Detection
+from batdetect2.postprocess.types import Detection
 
 
 @pytest.fixture

tests/test_evaluate/test_tasks/test_classification.py

@@ -4,8 +4,8 @@ from soundevent import data
 
 from batdetect2.evaluate.tasks import build_task
 from batdetect2.evaluate.tasks.classification import ClassificationTaskConfig
-from batdetect2.typing import ClipDetections
+from batdetect2.postprocess.types import ClipDetections
-from batdetect2.typing.targets import TargetProtocol
+from batdetect2.targets.types import TargetProtocol
 
 
 def test_classification(

tests/test_evaluate/test_tasks/test_detection.py

@@ -4,8 +4,8 @@ from soundevent import data
 
 from batdetect2.evaluate.tasks import build_task
 from batdetect2.evaluate.tasks.detection import DetectionTaskConfig
-from batdetect2.typing import ClipDetections
+from batdetect2.postprocess.types import ClipDetections
-from batdetect2.typing.targets import TargetProtocol
+from batdetect2.targets.types import TargetProtocol
 
 
 def test_detection(

tests/test_models/test_backbones.py

@@ -13,7 +13,7 @@ from batdetect2.models.backbones import (
     build_backbone,
     load_backbone_config,
 )
-from batdetect2.typing.models import BackboneModel
+from batdetect2.models.types import BackboneModel
 
 
 def test_unet_backbone_config_defaults():

tests/test_models/test_detectors.py

@@ -7,7 +7,7 @@ from batdetect2.models.backbones import UNetBackboneConfig
 from batdetect2.models.detectors import Detector, build_detector
 from batdetect2.models.encoder import Encoder
 from batdetect2.models.heads import BBoxHead, ClassifierHead
-from batdetect2.typing.models import ModelOutput
+from batdetect2.models.types import ModelOutput
 
 
 @pytest.fixture

tests/test_outputs/test_transform/test_transform.py

@@ -3,7 +3,7 @@ from soundevent import data
 from soundevent.geometry import compute_bounds
 
 from batdetect2.outputs import build_output_transform
-from batdetect2.typing import ClipDetections, Detection
+from batdetect2.postprocess.types import ClipDetections, Detection
 
 
 def test_shift_time_to_clip_start(clip: data.Clip):

tests/test_postprocessing/test_decoding.py

@@ -14,7 +14,8 @@ from batdetect2.postprocess.decoding import (
     get_generic_tags,
     get_prediction_features,
 )
-from batdetect2.typing import Detection, TargetProtocol
+from batdetect2.postprocess.types import Detection
+from batdetect2.targets.types import TargetProtocol
 
 
 @pytest.fixture

tests/test_train/test_lightning.py

@@ -8,6 +8,7 @@ from torch.optim import Adam
 from torch.optim.lr_scheduler import CosineAnnealingLR
 
 from batdetect2.api_v2 import BatDetect2API
+from batdetect2.audio.types import AudioLoader
 from batdetect2.config import BatDetect2Config
 from batdetect2.models import ModelConfig
 from batdetect2.train import (
@@ -19,7 +20,6 @@ from batdetect2.train import (
 from batdetect2.train.optimizers import AdamOptimizerConfig
 from batdetect2.train.schedulers import CosineAnnealingSchedulerConfig
 from batdetect2.train.train import build_training_module
-from batdetect2.typing.preprocess import AudioLoader
 
 
 def build_default_module(config: BatDetect2Config | None = None):