Moved decoding to outputs

src/batdetect2/api_v2.py

@@ -26,7 +26,7 @@ from batdetect2.outputs import (
     get_output_formatter,
 )
 from batdetect2.outputs.types import OutputFormatterProtocol
-from batdetect2.postprocess import build_postprocessor, to_raw_predictions
+from batdetect2.postprocess import build_postprocessor
 from batdetect2.postprocess.types import (
     ClipDetections,
     Detection,
@@ -215,13 +215,8 @@ class BatDetect2API:
         detections = self.model.postprocessor(
             outputs,
         )[0]
-        raw_predictions = to_raw_predictions(
-            detections.numpy(),
-            targets=self.targets,
-        )
-
-        return self.output_transform.transform_detections(
-            raw_predictions,
+        return self.output_transform.to_detections(
+            detections=detections,
             start_time=start_time,
         )
 
@@ -321,7 +316,8 @@ class BatDetect2API:
             config=config.outputs.format,
         )
         output_transform = build_output_transform(
-            config=config.outputs.transform
+            config=config.outputs.transform,
+            targets=targets,
         )
 
         return cls(
@@ -375,7 +371,8 @@ class BatDetect2API:
             config=config.outputs.format,
         )
         output_transform = build_output_transform(
-            config=config.outputs.transform
+            config=config.outputs.transform,
+            targets=targets,
         )
 
         return cls(

src/batdetect2/evaluate/evaluate.py

@@ -61,7 +61,10 @@ def run_evaluate(
         experiment_name=experiment_name,
         run_name=run_name,
     )
-    output_transform = build_output_transform(config=output_config.transform)
+    output_transform = build_output_transform(
+        config=output_config.transform,
+        targets=targets,
+    )
     module = EvaluationModule(
         model,
         evaluator,

src/batdetect2/evaluate/lightning.py

@@ -9,7 +9,6 @@ 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.postprocess.types import ClipDetections
 
 
@@ -24,7 +23,9 @@ class EvaluationModule(LightningModule):
 
         self.model = model
         self.evaluator = evaluator
-        self.output_transform = output_transform or build_output_transform()
+        self.output_transform = output_transform or build_output_transform(
+            targets=evaluator.targets
+        )
 
         self.clip_annotations: List[data.ClipAnnotation] = []
         self.predictions: List[ClipDetections] = []
@@ -39,18 +40,14 @@ class EvaluationModule(LightningModule):
         outputs = self.model.detector(batch.spec)
         clip_detections = self.model.postprocessor(outputs)
         predictions = [
-            ClipDetections(
+            self.output_transform.to_clip_detections(
+                detections=clip_dets,
                 clip=clip_annotation.clip,
-                detections=to_raw_predictions(
-                    clip_dets.numpy(),
-                    targets=self.evaluator.targets,
-                ),
             )
             for clip_annotation, clip_dets in zip(
                 clip_annotations, clip_detections, strict=False
             )
         ]
-        predictions = self.output_transform(predictions)
 
         self.clip_annotations.extend(clip_annotations)
         self.predictions.extend(predictions)

src/batdetect2/inference/batch.py

@@ -44,6 +44,7 @@ def run_batch_inference(
     targets = targets or build_targets()
     output_transform = output_transform or build_output_transform(
         config=config.outputs.transform,
+        targets=targets,
     )
 
     loader = build_inference_loader(

src/batdetect2/inference/lightning.py

@@ -6,7 +6,6 @@ from torch.utils.data import DataLoader
 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.postprocess.types import ClipDetections
 
 
@@ -18,7 +17,9 @@ class InferenceModule(LightningModule):
     ):
         super().__init__()
         self.model = model
-        self.output_transform = output_transform or build_output_transform()
+        self.output_transform = output_transform or build_output_transform(
+            targets=model.targets
+        )
 
     def predict_step(
         self,
@@ -34,19 +35,14 @@ class InferenceModule(LightningModule):
 
         clip_detections = self.model.postprocessor(outputs)
 
-        predictions = [
-            ClipDetections(
+        return [
+            self.output_transform.to_clip_detections(
+                detections=clip_dets,
                 clip=clip,
-                detections=to_raw_predictions(
-                    clip_dets.numpy(),
-                    targets=self.model.targets,
-                ),
             )
-            for clip, clip_dets in zip(clips, clip_detections, strict=False)
+            for clip, clip_dets in zip(clips, clip_detections, strict=True)
         ]
 
-        return self.output_transform(predictions)
-
     def get_dataset(self) -> InferenceDataset:
         dataloaders = self.trainer.predict_dataloaders
         assert isinstance(dataloaders, DataLoader)

src/batdetect2/outputs/__init__.py

@@ -11,9 +11,9 @@ from batdetect2.outputs.formats import (
 )
 from batdetect2.outputs.transforms import (
     OutputTransformConfig,
-    OutputTransformProtocol,
     build_output_transform,
 )
+from batdetect2.outputs.types import OutputTransformProtocol
 
 __all__ = [
     "BatDetect2OutputConfig",

src/batdetect2/outputs/transforms.py

@@ -1,89 +0,0 @@
-from collections.abc import Sequence
-from dataclasses import replace
-from typing import Protocol
-
-from soundevent.geometry import shift_geometry
-
-from batdetect2.core.configs import BaseConfig
-from batdetect2.postprocess.types import ClipDetections, Detection
-
-__all__ = [
-    "OutputTransform",
-    "OutputTransformConfig",
-    "OutputTransformProtocol",
-    "build_output_transform",
-]
-
-
-class OutputTransformConfig(BaseConfig):
-    shift_time_to_clip_start: bool = True
-
-
-class OutputTransformProtocol(Protocol):
-    def __call__(
-        self,
-        predictions: Sequence[ClipDetections],
-    ) -> list[ClipDetections]: ...
-
-    def transform_detections(
-        self,
-        detections: Sequence[Detection],
-        start_time: float = 0,
-    ) -> list[Detection]: ...
-
-
-def shift_detection_time(detection: Detection, time: float) -> Detection:
-    geometry = shift_geometry(detection.geometry, time=time)
-    return replace(detection, geometry=geometry)
-
-
-class OutputTransform(OutputTransformProtocol):
-    def __init__(self, shift_time_to_clip_start: bool = True):
-        self.shift_time_to_clip_start = shift_time_to_clip_start
-
-    def __call__(
-        self,
-        predictions: Sequence[ClipDetections],
-    ) -> list[ClipDetections]:
-        return [
-            self.transform_prediction(prediction) for prediction in predictions
-        ]
-
-    def transform_prediction(
-        self, prediction: ClipDetections
-    ) -> ClipDetections:
-        if not self.shift_time_to_clip_start:
-            return prediction
-
-        detections = self.transform_detections(
-            prediction.detections,
-            start_time=prediction.clip.start_time,
-        )
-        return ClipDetections(clip=prediction.clip, detections=detections)
-
-    def transform_detections(
-        self,
-        detections: Sequence[Detection],
-        start_time: float = 0,
-    ) -> list[Detection]:
-        if not self.shift_time_to_clip_start or start_time == 0:
-            return list(detections)
-
-        return [
-            shift_detection_time(detection, time=start_time)
-            for detection in detections
-        ]
-
-
-def build_output_transform(
-    config: OutputTransformConfig | dict | None = None,
-) -> OutputTransformProtocol:
-    if config is None:
-        config = OutputTransformConfig()
-
-    if not isinstance(config, OutputTransformConfig):
-        config = OutputTransformConfig.model_validate(config)
-
-    return OutputTransform(
-        shift_time_to_clip_start=config.shift_time_to_clip_start,
-    )

src/batdetect2/outputs/transforms/__init__.py

@@ -0,0 +1,173 @@
+from collections.abc import Sequence
+
+from pydantic import Field
+from soundevent import data
+
+from batdetect2.core.configs import BaseConfig
+from batdetect2.outputs.transforms.clip_transforms import (
+    ClipDetectionsTransformConfig,
+)
+from batdetect2.outputs.transforms.clip_transforms import (
+    clip_transforms as clip_transform_registry,
+)
+from batdetect2.outputs.transforms.decoding import to_detections
+from batdetect2.outputs.transforms.detection_transforms import (
+    DetectionTransformConfig,
+    shift_detections_to_start_time,
+)
+from batdetect2.outputs.transforms.detection_transforms import (
+    detection_transforms as detection_transform_registry,
+)
+from batdetect2.outputs.types import (
+    ClipDetectionsTransform,
+    DetectionTransform,
+    OutputTransformProtocol,
+)
+from batdetect2.postprocess.types import (
+    ClipDetections,
+    ClipDetectionsTensor,
+    Detection,
+)
+from batdetect2.targets.types import TargetProtocol
+
+__all__ = [
+    "ClipDetectionsTransformConfig",
+    "DetectionTransformConfig",
+    "OutputTransform",
+    "OutputTransformConfig",
+    "build_output_transform",
+]
+
+
+class OutputTransformConfig(BaseConfig):
+    detection_transforms: list[DetectionTransformConfig] = Field(
+        default_factory=list
+    )
+    clip_transforms: list[ClipDetectionsTransformConfig] = Field(
+        default_factory=list
+    )
+
+
+class OutputTransform(OutputTransformProtocol):
+    detection_transform_steps: list[DetectionTransform]
+    clip_transform_steps: list[ClipDetectionsTransform]
+
+    def __init__(
+        self,
+        targets: TargetProtocol,
+        detection_transform_steps: Sequence[DetectionTransform] = (),
+        clip_transform_steps: Sequence[ClipDetectionsTransform] = (),
+    ):
+        self.targets = targets
+        self.detection_transform_steps = list(detection_transform_steps)
+        self.clip_transform_steps = list(clip_transform_steps)
+
+    def __call__(
+        self,
+        predictions: Sequence[ClipDetections],
+    ) -> list[ClipDetections]:
+        return [
+            self._transform_prediction(prediction)
+            for prediction in predictions
+        ]
+
+    def _transform_prediction(
+        self,
+        prediction: ClipDetections,
+    ) -> ClipDetections:
+        detections = shift_detections_to_start_time(
+            prediction.detections,
+            start_time=prediction.clip.start_time,
+        )
+        detections = self.transform_detections(detections)
+        return self.transform_clip_detections(
+            ClipDetections(clip=prediction.clip, detections=detections)
+        )
+
+    def to_detections(
+        self,
+        detections: ClipDetectionsTensor,
+        start_time: float = 0,
+    ) -> list[Detection]:
+        decoded = to_detections(detections.numpy(), targets=self.targets)
+        shifted = shift_detections_to_start_time(
+            decoded,
+            start_time=start_time,
+        )
+        return self.transform_detections(shifted)
+
+    def to_clip_detections(
+        self,
+        detections: ClipDetectionsTensor,
+        clip: data.Clip,
+    ) -> ClipDetections:
+        prediction = ClipDetections(
+            clip=clip,
+            detections=self.to_detections(
+                detections,
+                start_time=clip.start_time,
+            ),
+        )
+        return self.transform_clip_detections(prediction)
+
+    def transform_detections(
+        self,
+        detections: Sequence[Detection],
+    ) -> list[Detection]:
+        out: list[Detection] = []
+        for detection in detections:
+            transformed = self.transform_detection(detection)
+
+            if transformed is None:
+                continue
+
+            out.append(transformed)
+
+        return []
+
+    def transform_detection(
+        self,
+        detection: Detection,
+    ) -> Detection | None:
+        for transform in self.detection_transform_steps:
+            detection = transform(detection)  # type: ignore
+
+            if detection is None:
+                return None
+
+        return detection
+
+    def transform_clip_detections(
+        self,
+        prediction: ClipDetections,
+    ) -> ClipDetections:
+        for transform in self.clip_transform_steps:
+            prediction = transform(prediction)
+        return prediction
+
+
+def build_output_transform(
+    config: OutputTransformConfig | dict | None = None,
+    targets: TargetProtocol | None = None,
+) -> OutputTransformProtocol:
+    from batdetect2.targets import build_targets
+
+    if config is None:
+        config = OutputTransformConfig()
+
+    if not isinstance(config, OutputTransformConfig):
+        config = OutputTransformConfig.model_validate(config)
+
+    targets = targets or build_targets()
+
+    return OutputTransform(
+        targets=targets,
+        detection_transform_steps=[
+            detection_transform_registry.build(transform_config)
+            for transform_config in config.detection_transforms
+        ],
+        clip_transform_steps=[
+            clip_transform_registry.build(transform_config)
+            for transform_config in config.clip_transforms
+        ],
+    )

src/batdetect2/outputs/transforms/clip_transforms.py

@@ -0,0 +1,31 @@
+from typing import Annotated, Literal
+
+from pydantic import Field
+
+from batdetect2.core.registries import (
+    ImportConfig,
+    Registry,
+    add_import_config,
+)
+from batdetect2.outputs.types import ClipDetectionsTransform
+
+__all__ = [
+    "ClipDetectionsTransformConfig",
+    "clip_transforms",
+]
+
+
+clip_transforms: Registry[ClipDetectionsTransform, []] = Registry(
+    "clip_detection_transform"
+)
+
+
+@add_import_config(clip_transforms)
+class ClipDetectionsTransformImportConfig(ImportConfig):
+    name: Literal["import"] = "import"
+
+
+ClipDetectionsTransformConfig = Annotated[
+    ClipDetectionsTransformImportConfig,
+    Field(discriminator="name"),
+]

src/batdetect2/outputs/transforms/decoding.py

@@ -1,33 +1,28 @@
-"""Decodes extracted detection data into standard soundevent predictions."""
+"""Decode extracted tensors into output-friendly detection objects."""
 
 from typing import List
 
 import numpy as np
 from soundevent import data
 
-from batdetect2.postprocess.types import (
-    ClipDetectionsArray,
-    Detection,
-)
+from batdetect2.postprocess.types import ClipDetectionsArray, Detection
 from batdetect2.targets.types import TargetProtocol
 
 __all__ = [
-    "to_raw_predictions",
-    "convert_raw_predictions_to_clip_prediction",
-    "convert_raw_prediction_to_sound_event_prediction",
     "DEFAULT_CLASSIFICATION_THRESHOLD",
+    "convert_raw_prediction_to_sound_event_prediction",
+    "convert_raw_predictions_to_clip_prediction",
+    "get_class_tags",
+    "get_generic_tags",
+    "get_prediction_features",
+    "to_detections",
 ]
 
 
 DEFAULT_CLASSIFICATION_THRESHOLD = 0.1
-"""Default threshold applied to classification scores.
-
-Class predictions with scores below this value are typically ignored during
-decoding.
-"""
 
 
-def to_raw_predictions(
+def to_detections(
     detections: ClipDetectionsArray,
     targets: TargetProtocol,
 ) -> List[Detection]:
@@ -69,7 +64,6 @@ def convert_raw_predictions_to_clip_prediction(
     classification_threshold: float = DEFAULT_CLASSIFICATION_THRESHOLD,
     top_class_only: bool = False,
 ) -> data.ClipPrediction:
-    """Convert a list of RawPredictions into a soundevent ClipPrediction."""
     return data.ClipPrediction(
         clip=clip,
         sound_events=[
@@ -92,7 +86,6 @@ def convert_raw_prediction_to_sound_event_prediction(
     classification_threshold: float | None = DEFAULT_CLASSIFICATION_THRESHOLD,
     top_class_only: bool = False,
 ):
-    """Convert a single RawPrediction into a soundevent SoundEventPrediction."""
     sound_event = data.SoundEvent(
         recording=recording,
         geometry=raw_prediction.geometry,
@@ -123,7 +116,6 @@ def get_generic_tags(
     detection_score: float,
     generic_class_tags: List[data.Tag],
 ) -> List[data.PredictedTag]:
-    """Create PredictedTag objects for the generic category."""
     return [
         data.PredictedTag(tag=tag, score=detection_score)
         for tag in generic_class_tags
@@ -131,7 +123,6 @@ def get_generic_tags(
 
 
 def get_prediction_features(features: np.ndarray) -> List[data.Feature]:
-    """Convert an extracted feature vector DataArray into soundevent Features."""
     return [
         data.Feature(
             term=data.Term(
@@ -151,39 +142,11 @@ def get_class_tags(
     top_class_only: bool = False,
     threshold: float | None = DEFAULT_CLASSIFICATION_THRESHOLD,
 ) -> List[data.PredictedTag]:
-    """Generate specific PredictedTags based on class scores and decoder.
-
-    Filters class scores by the threshold, sorts remaining scores descending,
-    decodes the class name(s) into base tags using the `sound_event_decoder`,
-    and creates `PredictedTag` objects associating the class score. Stops after
-    the first (top) class if `top_class_only` is True.
-
-    Parameters
-    ----------
-    class_scores : xr.DataArray
-        A 1D xarray DataArray containing class probabilities/scores, indexed
-        by a 'category' coordinate holding the class names.
-    sound_event_decoder : SoundEventDecoder
-        Function to map a class name string to a list of base `data.Tag`
-        objects.
-    top_class_only : bool, default=False
-        If True, only generate tags for the single highest-scoring class above
-        the threshold.
-    threshold : float, optional
-        Minimum score for a class to be considered. If None, all classes are
-        processed (or top-1 if `top_class_only` is True). Defaults to
-        `DEFAULT_CLASSIFICATION_THRESHOLD`.
-
-    Returns
-    -------
-    List[data.PredictedTag]
-        A list of `PredictedTag` objects for the class(es) that passed the
-        threshold, ordered by score if `top_class_only` is False.
-    """
     tags = []
 
     for class_name, score in _iterate_sorted(
-        class_scores, targets.class_names
+        class_scores,
+        targets.class_names,
     ):
         if threshold is not None and score < threshold:
             continue

src/batdetect2/outputs/transforms/detection_transforms.py

@@ -0,0 +1,55 @@
+from collections.abc import Sequence
+from dataclasses import replace
+from typing import Annotated, Literal
+
+from pydantic import Field
+from soundevent.geometry import shift_geometry
+
+from batdetect2.core.registries import (
+    ImportConfig,
+    Registry,
+    add_import_config,
+)
+from batdetect2.outputs.types import DetectionTransform
+from batdetect2.postprocess.types import Detection
+
+__all__ = [
+    "DetectionTransformConfig",
+    "detection_transforms",
+    "shift_detection_time",
+    "shift_detections_to_start_time",
+]
+
+
+detection_transforms: Registry[DetectionTransform, []] = Registry(
+    "detection_transform"
+)
+
+
+@add_import_config(detection_transforms)
+class DetectionTransformImportConfig(ImportConfig):
+    name: Literal["import"] = "import"
+
+
+DetectionTransformConfig = Annotated[
+    DetectionTransformImportConfig,
+    Field(discriminator="name"),
+]
+
+
+def shift_detection_time(detection: Detection, time: float) -> Detection:
+    geometry = shift_geometry(detection.geometry, time=time)
+    return replace(detection, geometry=geometry)
+
+
+def shift_detections_to_start_time(
+    detections: Sequence[Detection],
+    start_time: float = 0,
+) -> list[Detection]:
+    if start_time == 0:
+        return list(detections)
+
+    return [
+        shift_detection_time(detection, time=start_time)
+        for detection in detections
+    ]

src/batdetect2/outputs/types.py

@@ -1,12 +1,20 @@
-from collections.abc import Sequence
+from collections.abc import Callable, Sequence
 from typing import Generic, Protocol, TypeVar
 
+from soundevent import data
 from soundevent.data import PathLike
 
-from batdetect2.postprocess.types import ClipDetections
+from batdetect2.postprocess.types import (
+    ClipDetections,
+    ClipDetectionsTensor,
+    Detection,
+)
 
 __all__ = [
+    "ClipDetectionsTransform",
+    "DetectionTransform",
     "OutputFormatterProtocol",
+    "OutputTransformProtocol",
 ]
 
 T = TypeVar("T")
@@ -23,3 +31,31 @@ class OutputFormatterProtocol(Protocol, Generic[T]):
     ) -> None: ...
 
     def load(self, path: PathLike) -> list[T]: ...
+
+
+DetectionTransform = Callable[[Detection], Detection | None]
+ClipDetectionsTransform = Callable[[ClipDetections], ClipDetections]
+
+
+class OutputTransformProtocol(Protocol):
+    def to_detections(
+        self,
+        detections: ClipDetectionsTensor,
+        start_time: float = 0,
+    ) -> list[Detection]: ...
+
+    def to_clip_detections(
+        self,
+        detections: ClipDetectionsTensor,
+        clip: data.Clip,
+    ) -> ClipDetections: ...
+
+    def transform_detections(
+        self,
+        detections: Sequence[Detection],
+    ) -> list[Detection]: ...
+
+    def transform_clip_detections(
+        self,
+        prediction: ClipDetections,
+    ) -> ClipDetections: ...

src/batdetect2/postprocess/__init__.py

@@ -4,10 +4,6 @@ from batdetect2.postprocess.config import (
     PostprocessConfig,
     load_postprocess_config,
 )
-from batdetect2.postprocess.decoding import (
-    convert_raw_predictions_to_clip_prediction,
-    to_raw_predictions,
-)
 from batdetect2.postprocess.nms import non_max_suppression
 from batdetect2.postprocess.postprocessor import (
     Postprocessor,
@@ -18,8 +14,6 @@ __all__ = [
     "PostprocessConfig",
     "Postprocessor",
     "build_postprocessor",
-    "convert_raw_predictions_to_clip_prediction",
-    "to_raw_predictions",
     "load_postprocess_config",
     "non_max_suppression",
 ]

src/batdetect2/postprocess/config.py

@@ -2,7 +2,6 @@ from pydantic import Field
 from soundevent import data
 
 from batdetect2.core.configs import BaseConfig, load_config
-from batdetect2.postprocess.decoding import DEFAULT_CLASSIFICATION_THRESHOLD
 from batdetect2.postprocess.nms import NMS_KERNEL_SIZE
 
 __all__ = [
@@ -11,6 +10,7 @@ __all__ = [
 ]
 
 DEFAULT_DETECTION_THRESHOLD = 0.01
+DEFAULT_CLASSIFICATION_THRESHOLD = 0.1
 
 
 TOP_K_PER_SEC = 100

src/batdetect2/train/callbacks.py

@@ -9,7 +9,6 @@ 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
@@ -25,7 +24,7 @@ class ValidationMetrics(Callback):
         super().__init__()
 
         self.evaluator = evaluator
-        self.output_transform = output_transform or build_output_transform()
+        self.output_transform = output_transform
 
         self._clip_annotations: List[data.ClipAnnotation] = []
         self._predictions: List[ClipDetections] = []
@@ -92,6 +91,14 @@ class ValidationMetrics(Callback):
         dataloader_idx: int = 0,
     ) -> None:
         model = pl_module.model
+        if self.output_transform is None:
+            self.output_transform = build_output_transform(
+                targets=model.targets
+            )
+
+        output_transform = self.output_transform
+        assert output_transform is not None
+
         dataset = self.get_dataset(trainer)
 
         clip_annotations = [
@@ -101,17 +108,14 @@ class ValidationMetrics(Callback):
 
         clip_detections = model.postprocessor(outputs)
         predictions = [
-            ClipDetections(
+            output_transform.to_clip_detections(
+                detections=clip_dets,
                 clip=clip_annotation.clip,
-                detections=to_raw_predictions(
-                    clip_dets.numpy(), targets=model.targets
-                ),
             )
             for clip_annotation, clip_dets in zip(
                 clip_annotations, clip_detections, strict=False
             )
         ]
-        predictions = self.output_transform(predictions)
 
         self._clip_annotations.extend(clip_annotations)
         self._predictions.extend(predictions)

tests/test_outputs/test_transform/test_transform.py

@@ -1,12 +1,35 @@
+from dataclasses import replace
+
 import numpy as np
+import torch
 from soundevent import data
 from soundevent.geometry import compute_bounds
 
 from batdetect2.outputs import build_output_transform
-from batdetect2.postprocess.types import ClipDetections, Detection
+from batdetect2.outputs.transforms import OutputTransform
+from batdetect2.postprocess.types import (
+    ClipDetections,
+    ClipDetectionsTensor,
+    Detection,
+)
+from batdetect2.targets.types import TargetProtocol
 
 
-def test_shift_time_to_clip_start(clip: data.Clip):
+def _mock_clip_detections_tensor() -> ClipDetectionsTensor:
+    return ClipDetectionsTensor(
+        scores=torch.tensor([0.9], dtype=torch.float32),
+        sizes=torch.tensor([[0.1, 1_000.0]], dtype=torch.float32),
+        class_scores=torch.tensor([[0.8, 0.2]], dtype=torch.float32),
+        times=torch.tensor([0.2], dtype=torch.float32),
+        frequencies=torch.tensor([60_000.0], dtype=torch.float32),
+        features=torch.tensor([[1.0, 2.0]], dtype=torch.float32),
+    )
+
+
+def test_shift_time_to_clip_start(
+    clip: data.Clip,
+    sample_targets: TargetProtocol,
+):
     clip = clip.model_copy(update={"start_time": 2.5, "end_time": 3.0})
 
     detection = Detection(
@@ -16,7 +39,7 @@ def test_shift_time_to_clip_start(clip: data.Clip):
         features=np.array([1.0, 2.0]),
     )
 
-    transformed = build_output_transform()(
+    transformed = OutputTransform(targets=sample_targets)(
         [ClipDetections(clip=clip, detections=[detection])]
     )[0]
 
@@ -28,21 +51,71 @@ def test_shift_time_to_clip_start(clip: data.Clip):
     assert np.isclose(end_time, 2.7)
 
 
-def test_transform_identity_when_disabled(clip: data.Clip):
+def test_to_clip_detections_shifts_by_clip_start(
+    clip: data.Clip,
+    sample_targets: TargetProtocol,
+):
+    clip = clip.model_copy(update={"start_time": 2.5, "end_time": 3.0})
+    transform = build_output_transform(targets=sample_targets)
+    raw = _mock_clip_detections_tensor()
+
+    shifted = transform.to_clip_detections(detections=raw, clip=clip)
+    unshifted = transform.to_detections(detections=raw, start_time=0)
+
+    shifted_start, _, _, _ = compute_bounds(shifted.detections[0].geometry)
+    unshifted_start, _, _, _ = compute_bounds(unshifted[0].geometry)
+
+    assert np.isclose(shifted_start - unshifted_start, clip.start_time)
+
+
+def test_detection_and_clip_transforms_applied_in_order(
+    clip: data.Clip,
+    sample_targets: TargetProtocol,
+):
     clip = clip.model_copy(update={"start_time": 2.5, "end_time": 3.0})
 
-    detection = Detection(
+    detection_1 = Detection(
         geometry=data.BoundingBox(coordinates=[0.1, 10_000, 0.2, 12_000]),
-        detection_score=0.9,
+        detection_score=0.5,
+        class_scores=np.array([0.9]),
+        features=np.array([1.0, 2.0]),
+    )
+    detection_2 = Detection(
+        geometry=data.BoundingBox(coordinates=[0.2, 10_000, 0.3, 12_000]),
+        detection_score=0.7,
         class_scores=np.array([0.9]),
         features=np.array([1.0, 2.0]),
     )
 
-    transform = build_output_transform(
-        config={"shift_time_to_clip_start": False}
+    def boost_score(detection: Detection) -> Detection:
+        return replace(
+            detection,
+            detection_score=detection.detection_score + 0.2,
+        )
+
+    def keep_high_score(detection: Detection) -> Detection | None:
+        if detection.detection_score < 0.8:
+            return None
+        return detection
+
+    def tag_clip_transform(prediction: ClipDetections) -> ClipDetections:
+        detections = [
+            replace(detection, detection_score=1.0)
+            for detection in prediction.detections
+        ]
+        return replace(prediction, detections=detections)
+
+    transform = OutputTransform(
+        targets=sample_targets,
+        detection_transform_steps=[boost_score, keep_high_score],
+        clip_transform_steps=[tag_clip_transform],
     )
     transformed = transform(
-        [ClipDetections(clip=clip, detections=[detection])]
+        [ClipDetections(clip=clip, detections=[detection_1, detection_2])]
     )[0]
 
-    assert transformed.detections[0].geometry == detection.geometry
+    assert len(transformed.detections) == 1
+    assert transformed.detections[0].detection_score == 1.0
+
+    start_time, _, _, _ = compute_bounds(transformed.detections[0].geometry)
+    assert np.isclose(start_time, 2.7)

tests/test_postprocessing/test_decoding.py

@@ -6,7 +6,7 @@ import pytest
 import xarray as xr
 from soundevent import data
 
-from batdetect2.postprocess.decoding import (
+from batdetect2.outputs.transforms.decoding import (
     DEFAULT_CLASSIFICATION_THRESHOLD,
     convert_raw_prediction_to_sound_event_prediction,
     convert_raw_predictions_to_clip_prediction,