Refactor eval code

src/batdetect2/evaluate/__init__.py

@@ -2,14 +2,10 @@ from batdetect2.evaluate.config import (
     EvaluationConfig,
     load_evaluation_config,
 )
-from batdetect2.evaluate.match import (
+from batdetect2.evaluate.match import match_predictions_and_annotations
-    match_predictions_and_annotations,
-    match_sound_events_and_raw_predictions,
-)
 
 __all__ = [
     "EvaluationConfig",
     "load_evaluation_config",
     "match_predictions_and_annotations",
-    "match_sound_events_and_raw_predictions",
 ]

src/batdetect2/evaluate/match.py

@@ -3,6 +3,7 @@ from dataclasses import dataclass, field
 from typing import List, Literal, Optional, Protocol, Tuple
 
 import numpy as np
+from loguru import logger
 from soundevent import data
 from soundevent.evaluation import compute_affinity
 from soundevent.evaluation import match_geometries as optimal_match
@@ -10,10 +11,10 @@ from soundevent.geometry import compute_bounds
 
 from batdetect2.configs import BaseConfig
 from batdetect2.typing import (
-    BatDetect2Prediction,
     MatchEvaluation,
     TargetProtocol,
 )
+from batdetect2.typing.postprocess import RawPrediction
 
 MatchingStrategy = Literal["greedy", "optimal"]
 """The type of matching algorithm to use: 'greedy' or 'optimal'."""
@@ -274,7 +275,7 @@ def greedy_match(
 
 def match_sound_events_and_raw_predictions(
     clip_annotation: data.ClipAnnotation,
-    raw_predictions: List[BatDetect2Prediction],
+    raw_predictions: List[RawPrediction],
     targets: TargetProtocol,
     config: Optional[MatchConfig] = None,
 ) -> List[MatchEvaluation]:
@@ -294,12 +295,11 @@ def match_sound_events_and_raw_predictions(
     ]
 
     predicted_geometries = [
-        raw_prediction.raw.geometry for raw_prediction in raw_predictions
+        raw_prediction.geometry for raw_prediction in raw_predictions
     ]
 
     scores = [
-        raw_prediction.raw.detection_score
+        raw_prediction.detection_score for raw_prediction in raw_predictions
-        for raw_prediction in raw_predictions
     ]
 
     matches = []
@@ -320,14 +320,20 @@ def match_sound_events_and_raw_predictions(
         gt_det = target is not None
         gt_class = targets.encode_class(target) if target is not None else None
 
-        pred_score = float(prediction.raw.detection_score) if prediction else 0
+        pred_score = float(prediction.detection_score) if prediction else 0
+
+        pred_geometry = (
+            predicted_geometries[source_idx]
+            if source_idx is not None
+            else None
+        )
 
         class_scores = (
             {
                 str(class_name): float(score)
                 for class_name, score in zip(
                     targets.class_names,
-                    prediction.raw.class_scores,
+                    prediction.class_scores,
                 )
             }
             if prediction is not None
@@ -336,17 +342,14 @@ def match_sound_events_and_raw_predictions(
 
         matches.append(
             MatchEvaluation(
-                match=data.Match(
+                clip=clip_annotation.clip,
-                    source=None
+                sound_event_annotation=target,
-                    if prediction is None
-                    else prediction.sound_event_prediction,
-                    target=target,
-                    affinity=affinity,
-                ),
                 gt_det=gt_det,
                 gt_class=gt_class,
                 pred_score=pred_score,
                 pred_class_scores=class_scores,
+                pred_geometry=pred_geometry,
+                affinity=affinity,
             )
         )
 
@@ -418,6 +421,28 @@ def match_predictions_and_annotations(
     return matches
 
 
+def match_all_predictions(
+    clip_annotations: List[data.ClipAnnotation],
+    predictions: List[List[RawPrediction]],
+    targets: TargetProtocol,
+    config: Optional[MatchConfig] = None,
+) -> List[MatchEvaluation]:
+    logger.info("Matching all annotations and predictions...")
+    return [
+        match
+        for clip_annotation, raw_predictions in zip(
+            clip_annotations,
+            predictions,
+        )
+        for match in match_sound_events_and_raw_predictions(
+            clip_annotation,
+            raw_predictions,
+            targets=targets,
+            config=config,
+        )
+    ]
+
+
 @dataclass
 class ClassExamples:
     false_positives: List[MatchEvaluation] = field(default_factory=list)

src/batdetect2/models/__init__.py

@@ -68,7 +68,7 @@ from batdetect2.postprocess import PostprocessConfig, build_postprocessor
 from batdetect2.preprocess import PreprocessingConfig, build_preprocessor
 from batdetect2.targets import TargetConfig, build_targets
 from batdetect2.typing.models import DetectionModel
-from batdetect2.typing.postprocess import Detections, PostprocessorProtocol
+from batdetect2.typing.postprocess import DetectionsArray, PostprocessorProtocol
 from batdetect2.typing.preprocess import PreprocessorProtocol
 from batdetect2.typing.targets import TargetProtocol
 
@@ -122,7 +122,7 @@ class Model(LightningModule):
         self.targets = targets
         self.save_hyperparameters()
 
-    def forward(self, wav: torch.Tensor) -> List[Detections]:
+    def forward(self, wav: torch.Tensor) -> List[DetectionsArray]:
         spec = self.preprocessor(wav)
         outputs = self.detector(spec)
         return self.postprocessor(outputs)

src/batdetect2/plotting/matches.py

@@ -124,25 +124,21 @@ def plot_false_positive_match(
     add_points: bool = False,
     fill: bool = False,
     spec_cmap: str = "gray",
-    time_offset: float = 0,
     color: str = DEFAULT_FALSE_POSITIVE_COLOR,
     fontsize: Union[float, str] = "small",
 ) -> Axes:
-    assert match.match.source is not None
+    assert match.pred_geometry is not None
-    assert match.match.target is None
+    assert match.sound_event_annotation is None
-    sound_event = match.match.source.sound_event
-    geometry = sound_event.geometry
-    assert geometry is not None
 
-    start_time, _, _, high_freq = compute_bounds(geometry)
+    start_time, _, _, high_freq = compute_bounds(match.pred_geometry)
 
     clip = data.Clip(
         start_time=max(start_time - duration / 2, 0),
         end_time=min(
             start_time + duration / 2,
-            sound_event.recording.duration,
+            match.clip.end_time,
         ),
-        recording=sound_event.recording,
+        recording=match.clip.recording,
     )
 
     ax = plot_clip(
@@ -154,11 +150,9 @@ def plot_false_positive_match(
         spec_cmap=spec_cmap,
     )
 
-    plot_prediction(
+    plot.plot_geometry(
-        match.match.source,
+        match.pred_geometry,
         ax=ax,
-        time_offset=time_offset,
-        freq_offset=2_000,
         add_points=add_points,
         facecolor="none" if not fill else None,
         alpha=1,
@@ -191,9 +185,9 @@ def plot_false_negative_match(
     color: str = DEFAULT_FALSE_NEGATIVE_COLOR,
     fontsize: Union[float, str] = "small",
 ) -> Axes:
-    assert match.match.source is None
+    assert match.pred_geometry is None
-    assert match.match.target is not None
+    assert match.sound_event_annotation is not None
-    sound_event = match.match.target.sound_event
+    sound_event = match.sound_event_annotation.sound_event
     geometry = sound_event.geometry
     assert geometry is not None
 
@@ -217,7 +211,7 @@ def plot_false_negative_match(
     )
 
     plot.plot_annotation(
-        match.match.target,
+        match.sound_event_annotation,
         ax=ax,
         time_offset=0.001,
         freq_offset=2_000,
@@ -255,9 +249,9 @@ def plot_true_positive_match(
     annotation_linestyle: str = DEFAULT_ANNOTATION_LINE_STYLE,
     prediction_linestyle: str = DEFAULT_PREDICTION_LINE_STYLE,
 ) -> Axes:
-    assert match.match.source is not None
+    assert match.sound_event_annotation is not None
-    assert match.match.target is not None
+    assert match.pred_geometry is not None
-    sound_event = match.match.target.sound_event
+    sound_event = match.sound_event_annotation.sound_event
     geometry = sound_event.geometry
     assert geometry is not None
 
@@ -281,7 +275,7 @@ def plot_true_positive_match(
     )
 
     plot.plot_annotation(
-        match.match.target,
+        match.sound_event_annotation,
         ax=ax,
         time_offset=0.001,
         freq_offset=2_000,
@@ -292,11 +286,9 @@ def plot_true_positive_match(
         linestyle=annotation_linestyle,
     )
 
-    plot_prediction(
+    plot.plot_geometry(
-        match.match.source,
+        match.pred_geometry,
         ax=ax,
-        time_offset=0.001,
-        freq_offset=2_000,
         add_points=add_points,
         facecolor="none" if not fill else None,
         alpha=1,
@@ -332,9 +324,9 @@ def plot_cross_trigger_match(
     annotation_linestyle: str = DEFAULT_ANNOTATION_LINE_STYLE,
     prediction_linestyle: str = DEFAULT_PREDICTION_LINE_STYLE,
 ) -> Axes:
-    assert match.match.source is not None
+    assert match.sound_event_annotation is not None
-    assert match.match.target is not None
+    assert match.pred_geometry is not None
-    sound_event = match.match.source.sound_event
+    sound_event = match.sound_event_annotation.sound_event
     geometry = sound_event.geometry
     assert geometry is not None
 
@@ -358,7 +350,7 @@ def plot_cross_trigger_match(
     )
 
     plot.plot_annotation(
-        match.match.target,
+        match.sound_event_annotation,
         ax=ax,
         time_offset=0.001,
         freq_offset=2_000,
@@ -369,11 +361,9 @@ def plot_cross_trigger_match(
         linestyle=annotation_linestyle,
     )
 
-    plot_prediction(
+    plot.plot_geometry(
-        match.match.source,
+        match.pred_geometry,
         ax=ax,
-        time_offset=0.001,
-        freq_offset=2_000,
         add_points=add_points,
         facecolor="none" if not fill else None,
         alpha=1,

src/batdetect2/postprocess/__init__.py

@@ -10,9 +10,9 @@ from soundevent import data
 from batdetect2.configs import BaseConfig, load_config
 from batdetect2.postprocess.decoding import (
     DEFAULT_CLASSIFICATION_THRESHOLD,
-    convert_detections_to_raw_predictions,
     convert_raw_prediction_to_sound_event_prediction,
     convert_raw_predictions_to_clip_prediction,
+    to_raw_predictions,
 )
 from batdetect2.postprocess.extraction import extract_prediction_tensor
 from batdetect2.postprocess.nms import (
@@ -24,7 +24,8 @@ from batdetect2.preprocess import MAX_FREQ, MIN_FREQ
 from batdetect2.typing import ModelOutput
 from batdetect2.typing.postprocess import (
     BatDetect2Prediction,
-    Detections,
+    DetectionsArray,
+    DetectionsTensor,
     PostprocessorProtocol,
     RawPrediction,
 )
@@ -43,7 +44,7 @@ __all__ = [
     "TOP_K_PER_SEC",
     "build_postprocessor",
     "convert_raw_predictions_to_clip_prediction",
-    "convert_detections_to_raw_predictions",
+    "to_raw_predictions",
     "load_postprocess_config",
     "non_max_suppression",
 ]
@@ -168,7 +169,7 @@ class Postprocessor(torch.nn.Module, PostprocessorProtocol):
         self.top_k_per_sec = top_k_per_sec
         self.detection_threshold = detection_threshold
 
-    def forward(self, output: ModelOutput) -> List[Detections]:
+    def forward(self, output: ModelOutput) -> List[DetectionsTensor]:
         width = output.detection_probs.shape[-1]
         duration = width / self.samplerate
         max_detections = int(self.top_k_per_sec * duration)
@@ -192,7 +193,7 @@ class Postprocessor(torch.nn.Module, PostprocessorProtocol):
         self,
         output: ModelOutput,
         clips: Optional[List[data.Clip]] = None,
-    ) -> List[Detections]:
+    ) -> List[DetectionsTensor]:
         width = output.detection_probs.shape[-1]
         duration = width / self.samplerate
         max_detections = int(self.top_k_per_sec * duration)
@@ -245,11 +246,8 @@ def get_raw_predictions(
     """
     detections = postprocessor.get_detections(output, clips)
     return [
-        convert_detections_to_raw_predictions(
+        to_raw_predictions(detection.numpy(), targets=targets)
-            dataset,
+        for detection in detections
-            targets=targets,
-        )
-        for dataset in detections
     ]
 
 

src/batdetect2/postprocess/decoding.py

@@ -6,13 +6,13 @@ import numpy as np
 from soundevent import data
 
 from batdetect2.typing.postprocess import (
-    Detections,
+    DetectionsArray,
     RawPrediction,
 )
 from batdetect2.typing.targets import TargetProtocol
 
 __all__ = [
-    "convert_detections_to_raw_predictions",
+    "to_raw_predictions",
     "convert_raw_predictions_to_clip_prediction",
     "convert_raw_prediction_to_sound_event_prediction",
     "DEFAULT_CLASSIFICATION_THRESHOLD",
@@ -27,19 +27,19 @@ decoding.
 """
 
 
-def convert_detections_to_raw_predictions(
+def to_raw_predictions(
-    detections: Detections,
+    detections: DetectionsArray,
     targets: TargetProtocol,
 ) -> List[RawPrediction]:
     predictions = []
 
     for score, class_scores, time, freq, dims, feats in zip(
-        detections.scores.cpu().numpy(),
+        detections.scores,
-        detections.class_scores.cpu().numpy(),
+        detections.class_scores,
-        detections.times.cpu().numpy(),
+        detections.times,
-        detections.frequencies.cpu().numpy(),
+        detections.frequencies,
-        detections.sizes.cpu().numpy(),
+        detections.sizes,
-        detections.features.cpu().numpy(),
+        detections.features,
     ):
         highest_scoring_class = targets.class_names[class_scores.argmax()]
 

src/batdetect2/postprocess/extraction.py

@@ -20,7 +20,10 @@ from typing import List, Optional, Tuple, Union
 import torch
 
 from batdetect2.postprocess.nms import NMS_KERNEL_SIZE, non_max_suppression
-from batdetect2.typing.postprocess import Detections, ModelOutput
+from batdetect2.typing.postprocess import (
+    DetectionsTensor,
+    ModelOutput,
+)
 
 __all__ = [
     "extract_prediction_tensor",
@@ -32,7 +35,7 @@ def extract_prediction_tensor(
     max_detections: int = 200,
     threshold: Optional[float] = None,
     nms_kernel_size: Union[int, Tuple[int, int]] = NMS_KERNEL_SIZE,
-) -> List[Detections]:
+) -> List[DetectionsTensor]:
     detection_heatmap = non_max_suppression(
         output.detection_probs.detach(),
         kernel_size=nms_kernel_size,
@@ -78,7 +81,7 @@ def extract_prediction_tensor(
             class_scores = class_scores[mask]
 
         predictions.append(
-            Detections(
+            DetectionsTensor(
                 scores=detection_scores,
                 sizes=sizes,
                 features=features,

src/batdetect2/postprocess/remapping.py

@@ -20,7 +20,7 @@ import xarray as xr
 from soundevent.arrays import Dimensions
 
 from batdetect2.preprocess import MAX_FREQ, MIN_FREQ
-from batdetect2.typing.postprocess import Detections
+from batdetect2.typing.postprocess import DetectionsTensor
 
 __all__ = [
     "features_to_xarray",
@@ -31,15 +31,15 @@ __all__ = [
 
 
 def map_detection_to_clip(
-    detections: Detections,
+    detections: DetectionsTensor,
     start_time: float,
     end_time: float,
     min_freq: float,
     max_freq: float,
-) -> Detections:
+) -> DetectionsTensor:
     duration = end_time - start_time
     bandwidth = max_freq - min_freq
-    return Detections(
+    return DetectionsTensor(
         scores=detections.scores,
         sizes=detections.sizes,
         features=detections.features,

src/batdetect2/targets/__init__.py

@@ -21,7 +21,7 @@ configured processing steps. The main way to create a functional `Targets`
 object is via the `build_targets` or `load_targets` functions.
 """
 
-from typing import List, Optional
+from typing import Iterable, List, Optional, Tuple
 
 from loguru import logger
 from pydantic import Field
@@ -675,3 +675,24 @@ def load_targets(
         term_registry=term_registry,
         derivation_registry=derivation_registry,
     )
+
+
+def iterate_encoded_sound_events(
+    sound_events: Iterable[data.SoundEventAnnotation],
+    targets: TargetProtocol,
+) -> Iterable[Tuple[Optional[str], Position, Size]]:
+    for sound_event in sound_events:
+        if not targets.filter(sound_event):
+            continue
+
+        geometry = sound_event.sound_event.geometry
+
+        if geometry is None:
+            continue
+
+        sound_event = targets.transform(sound_event)
+
+        class_name = targets.encode_class(sound_event)
+        position, size = targets.encode_roi(sound_event)
+
+        yield class_name, position, size

src/batdetect2/train/callbacks.py

@@ -1,32 +1,26 @@
-import io
+from typing import List, Optional
-from typing import List, Optional, Tuple
 
-import numpy as np
 from lightning import LightningModule, Trainer
 from lightning.pytorch.callbacks import Callback
-from lightning.pytorch.loggers import Logger, TensorBoardLogger
-from lightning.pytorch.loggers.mlflow import MLFlowLogger
-from loguru import logger
 from soundevent import data
 from torch.utils.data import DataLoader
 
 from batdetect2.evaluate.match import (
     MatchConfig,
-    match_sound_events_and_raw_predictions,
+    match_all_predictions,
 )
-from batdetect2.models import Model
 from batdetect2.plotting.evaluation import plot_example_gallery
-from batdetect2.postprocess import get_sound_event_predictions
+from batdetect2.postprocess import get_raw_predictions
-from batdetect2.train.dataset import TrainingDataset
+from batdetect2.train.dataset import ValidationDataset
 from batdetect2.train.lightning import TrainingModule
+from batdetect2.train.logging import get_image_plotter
 from batdetect2.typing import (
-    BatDetect2Prediction,
     MatchEvaluation,
     MetricsProtocol,
-    ModelOutput,
-    TargetProtocol,
-    TrainExample,
 )
+from batdetect2.typing.models import ModelOutput
+from batdetect2.typing.postprocess import RawPrediction
+from batdetect2.typing.train import TrainExample
 
 
 class ValidationMetrics(Callback):
@@ -45,15 +39,14 @@ class ValidationMetrics(Callback):
         self.metrics = metrics
         self.plot = plot
 
-        self._matches: List[
+        self._clip_annotations: List[data.ClipAnnotation] = []
-            Tuple[data.ClipAnnotation, List[BatDetect2Prediction]]
+        self._predictions: List[List[RawPrediction]] = []
-        ] = []
 
-    def get_dataset(self, trainer: Trainer) -> TrainingDataset:
+    def get_dataset(self, trainer: Trainer) -> ValidationDataset:
         dataloaders = trainer.val_dataloaders
         assert isinstance(dataloaders, DataLoader)
         dataset = dataloaders.dataset
-        assert isinstance(dataset, TrainingDataset)
+        assert isinstance(dataset, ValidationDataset)
         return dataset
 
     def plot_examples(
@@ -61,7 +54,7 @@ class ValidationMetrics(Callback):
         pl_module: LightningModule,
         matches: List[MatchEvaluation],
     ):
-        plotter = _get_image_plotter(pl_module.logger)  # type: ignore
+        plotter = get_image_plotter(pl_module.logger)  # type: ignore
 
         if plotter is None:
             return
@@ -93,9 +86,10 @@ class ValidationMetrics(Callback):
         trainer: Trainer,
         pl_module: LightningModule,
     ) -> None:
-        matches = _match_all_collected_examples(
+        matches = match_all_predictions(
-            self._matches,
+            self._clip_annotations,
-            pl_module.model.targets,
+            self._predictions,
+            targets=pl_module.model.targets,
             config=self.match_config,
         )
 
@@ -123,133 +117,23 @@ class ValidationMetrics(Callback):
         batch_idx: int,
         dataloader_idx: int = 0,
     ) -> None:
-        self._matches.extend(
+        postprocessor = pl_module.model.postprocessor
-            _get_batch_clips_and_predictions(
+        targets = pl_module.model.targets
-                batch,
+        dataset = self.get_dataset(trainer)
-                outputs,
-                dataset=self.get_dataset(trainer),
-                model=pl_module.model,
-            )
-        )
 
+        clip_annotations = [
+            dataset.clip_annotations[int(example_idx)]
+            for example_idx in batch.idx
+        ]
 
-def _get_batch_clips_and_predictions(
+        predictions = get_raw_predictions(
-    batch: TrainExample,
+            outputs,
-    outputs: ModelOutput,
+            clips=[
-    dataset: TrainingDataset,
+                clip_annotation.clip for clip_annotation in clip_annotations
-    model: Model,
+            ],
-) -> List[Tuple[data.ClipAnnotation, List[BatDetect2Prediction]]]:
-    clip_annotations = [
-        _get_subclip(
-            dataset.clip_annotations[int(example_id)],
-            start_time=start_time.item(),
-            end_time=end_time.item(),
-            targets=model.targets,
-        )
-        for example_id, start_time, end_time in zip(
-            batch.idx,
-            batch.start_time,
-            batch.end_time,
-        )
-    ]
-
-    clips = [clip_annotation.clip for clip_annotation in clip_annotations]
-
-    raw_predictions = get_sound_event_predictions(
-        outputs,
-        clips,
-        targets=model.targets,
-        postprocessor=model.postprocessor
-    )
-
-    return [
-        (clip_annotation, clip_predictions)
-        for clip_annotation, clip_predictions in zip(
-            clip_annotations, raw_predictions
-        )
-    ]
-
-
-def _match_all_collected_examples(
-    pre_matches: List[Tuple[data.ClipAnnotation, List[BatDetect2Prediction]]],
-    targets: TargetProtocol,
-    config: Optional[MatchConfig] = None,
-) -> List[MatchEvaluation]:
-    logger.info("Matching all annotations and predictions...")
-    return [
-        match
-        for clip_annotation, raw_predictions in pre_matches
-        for match in match_sound_events_and_raw_predictions(
-            clip_annotation,
-            raw_predictions,
             targets=targets,
-            config=config,
+            postprocessor=postprocessor,
         )
-    ]
 
-
+        self._clip_annotations.extend(clip_annotations)
-def _is_in_subclip(
+        self._predictions.extend(predictions)
-    sound_event_annotation: data.SoundEventAnnotation,
-    targets: TargetProtocol,
-    start_time: float,
-    end_time: float,
-) -> bool:
-    (time, _), _ = targets.encode_roi(sound_event_annotation)
-    return start_time <= time <= end_time
-
-
-def _get_subclip(
-    clip_annotation: data.ClipAnnotation,
-    start_time: float,
-    end_time: float,
-    targets: TargetProtocol,
-) -> data.ClipAnnotation:
-    return data.ClipAnnotation(
-        clip=data.Clip(
-            recording=clip_annotation.clip.recording,
-            start_time=start_time,
-            end_time=end_time,
-        ),
-        sound_events=[
-            sound_event_annotation
-            for sound_event_annotation in clip_annotation.sound_events
-            if _is_in_subclip(
-                sound_event_annotation,
-                targets,
-                start_time=start_time,
-                end_time=end_time,
-            )
-        ],
-    )
-
-
-def _get_image_plotter(logger: Logger):
-    if isinstance(logger, TensorBoardLogger):
-
-        def plot_figure(name, figure, step):
-            return logger.experiment.add_figure(name, figure, step)
-
-        return plot_figure
-
-    if isinstance(logger, MLFlowLogger):
-
-        def plot_figure(name, figure, step):
-            image = _convert_figure_to_image(figure)
-            return logger.experiment.log_image(
-                run_id=logger.run_id,
-                image=image,
-                key=name,
-                step=step,
-            )
-
-        return plot_figure
-
-
-def _convert_figure_to_image(figure):
-    with io.BytesIO() as buff:
-        figure.savefig(buff, format="raw")
-        buff.seek(0)
-        data = np.frombuffer(buff.getvalue(), dtype=np.uint8)
-    w, h = figure.canvas.get_width_height()
-    im = data.reshape((int(h), int(w), -1))
-    return im

src/batdetect2/train/dataset.py

@@ -6,7 +6,10 @@ from torch.utils.data import Dataset
 
 from batdetect2.typing import ClipperProtocol, TrainExample
 from batdetect2.typing.preprocess import AudioLoader, PreprocessorProtocol
-from batdetect2.typing.train import Augmentation, ClipLabeller
+from batdetect2.typing.train import (
+    Augmentation,
+    ClipLabeller,
+)
 
 __all__ = [
     "TrainingDataset",
@@ -75,3 +78,47 @@ class TrainingDataset(Dataset):
             start_time=torch.tensor(clip.start_time),
             end_time=torch.tensor(clip.end_time),
         )
+
+
+class ValidationDataset(Dataset):
+    def __init__(
+        self,
+        clip_annotations: Sequence[data.ClipAnnotation],
+        audio_loader: AudioLoader,
+        preprocessor: PreprocessorProtocol,
+        labeller: ClipLabeller,
+        audio_dir: Optional[data.PathLike] = None,
+    ):
+        self.clip_annotations = clip_annotations
+        self.labeller = labeller
+        self.preprocessor = preprocessor
+        self.audio_loader = audio_loader
+        self.audio_dir = audio_dir
+
+    def __len__(self):
+        return len(self.clip_annotations)
+
+    def __getitem__(self, idx) -> TrainExample:
+        clip_annotation = self.clip_annotations[idx]
+        clip = clip_annotation.clip
+
+        wav = self.audio_loader.load_clip(
+            clip_annotation.clip,
+            audio_dir=self.audio_dir,
+        )
+
+        wav_tensor = torch.tensor(wav).unsqueeze(0)
+
+        spectrogram = self.preprocessor(wav_tensor)
+
+        heatmaps = self.labeller(clip_annotation, spectrogram)
+
+        return TrainExample(
+            spec=spectrogram,
+            detection_heatmap=heatmaps.detection,
+            class_heatmap=heatmaps.classes,
+            size_heatmap=heatmaps.size,
+            idx=torch.tensor(idx),
+            start_time=torch.tensor(clip.start_time),
+            end_time=torch.tensor(clip.end_time),
+        )

src/batdetect2/train/labels.py

@@ -3,24 +3,6 @@
 This module is responsible for creating the target labels used for training
 BatDetect2 models. It converts sound event annotations for an audio clip into
 the specific multi-channel heatmap formats required by the neural network.
-
-It uses a pre-configured object adhering to the `TargetProtocol` (from
-`batdetect2.targets`) which encapsulates all the logic for filtering
-annotations, transforming tags, encoding class names, and mapping annotation
-geometry (ROIs) to target positions and sizes. This module then focuses on
-rendering this information onto the heatmap grids.
-
-The pipeline generates three core outputs for a given spectrogram:
-1.  **Detection Heatmap**: Indicates presence/location of relevant sound events.
-2.  **Class Heatmap**: Indicates location and class identity for specifically
-    classified events.
-3.  **Size Heatmap**: Encodes the target dimensions (width, height) of events.
-
-The primary function generated by this module is a `ClipLabeller` (defined in
-`.types`), which takes a `ClipAnnotation` object and its corresponding
-spectrogram and returns the calculated `Heatmaps` tuple. The main configurable
-parameter specific to this module is the Gaussian smoothing sigma (`sigma`)
-defined in `LabelConfig`.
 """
 
 from functools import partial
@@ -32,6 +14,7 @@ from loguru import logger
 from soundevent import data
 
 from batdetect2.configs import BaseConfig, load_config
+from batdetect2.targets import iterate_encoded_sound_events
 from batdetect2.typing import (
     ClipLabeller,
     Heatmaps,
@@ -56,9 +39,6 @@ class LabelConfig(BaseConfig):
     Attributes
     ----------
     sigma : float, default=3.0
-        The standard deviation (in pixels/bins) of the Gaussian kernel applied
-        to smooth the detection and class heatmaps. Larger values create more
-        diffuse targets.
     """
 
     sigma: float = 2.0
@@ -70,28 +50,7 @@ def build_clip_labeler(
     max_freq: float,
     config: Optional[LabelConfig] = None,
 ) -> ClipLabeller:
-    """Construct the final clip labelling function.
+    """Construct the final clip labelling function."""
-
-    This factory function prepares the callable that will perform the
-    end-to-end heatmap generation for a given clip and spectrogram during
-    training data loading. It takes the fully configured `targets` object and
-    the `LabelConfig` and binds them to the `generate_clip_label` function.
-
-    Parameters
-    ----------
-    targets : TargetProtocol
-        An initialized object conforming to the `TargetProtocol`, providing all
-        necessary methods for filtering, transforming, encoding, and ROI
-        mapping.
-    config : LabelConfig
-        Configuration object containing heatmap generation parameters.
-
-    Returns
-    -------
-    ClipLabeller
-        A function that accepts a `data.ClipAnnotation` and `xr.DataArray`
-        (spectrogram) and returns the generated `Heatmaps`.
-    """
     config = config or LabelConfig()
     logger.opt(lazy=True).debug(
         "Building clip labeler with config: \n{}",
@@ -119,37 +78,10 @@ def generate_heatmaps(
     target_sigma: float = 3.0,
     dtype=torch.float32,
 ) -> Heatmaps:
-    """Generate training heatmaps for a single annotated clip.
+    """Generate training heatmaps for a single annotated clip."""
-
-    This function orchestrates the target generation process for one clip:
-    1. Filters and transforms sound events using `targets.filter` and
-       `targets.transform`.
-    2. Passes the resulting processed annotations, along with the spectrogram,
-       the `targets` object, and the Gaussian `sigma` from `config`, to the
-       core `generate_heatmaps` function.
-
-    Parameters
-    ----------
-    clip_annotation : data.ClipAnnotation
-        The complete annotation data for the audio clip, including the list
-        of `sound_events` to process.
-    spec : xr.DataArray
-        The spectrogram corresponding to the `clip_annotation`. Must have
-        'time' and 'frequency' dimensions/coordinates.
-    targets : TargetProtocol
-        The fully configured target definition object, providing methods for
-        filtering, transformation, encoding, and ROI mapping.
-    config : LabelConfig
-        Configuration object providing heatmap parameters (primarily `sigma`).
-
-    Returns
-    -------
-    Heatmaps
-        A NamedTuple containing the generated 'detection', 'classes', and 'size'
-        heatmaps for this clip.
-    """
     logger.debug(
-        "Will generate heatmaps for clip annotation {uuid} with {num} annotated sound events",
+        "Will generate heatmaps for clip annotation "
+        "{uuid} with {num} annotated sound events",
         uuid=clip_annotation.uuid,
         num=len(clip_annotation.sound_events),
     )
@@ -174,28 +106,10 @@ def generate_heatmaps(
     freqs = freqs.to(spec.device)
     times = times.to(spec.device)
 
-    for sound_event_annotation in clip_annotation.sound_events:
+    for class_name, (time, frequency), size in iterate_encoded_sound_events(
-        if not targets.filter(sound_event_annotation):
+        clip_annotation.sound_events,
-            logger.debug(
+        targets,
-                "Sound event {sound_event} did not pass the filter. Tags: {tags}",
+    ):
-                sound_event=sound_event_annotation,
-                tags=sound_event_annotation.tags,
-            )
-            continue
-
-        sound_event_annotation = targets.transform(sound_event_annotation)
-
-        geom = sound_event_annotation.sound_event.geometry
-        if geom is None:
-            logger.debug(
-                "Skipping annotation %s: missing geometry.",
-                sound_event_annotation.uuid,
-            )
-            continue
-
-        # Get the position of the sound event
-        (time, frequency), size = targets.encode_roi(sound_event_annotation)
-
         time_index = map_to_pixels(time, width, clip.start_time, clip.end_time)
         freq_index = map_to_pixels(frequency, height, min_freq, max_freq)
 
@@ -206,9 +120,7 @@ def generate_heatmaps(
             or freq_index >= height
         ):
             logger.debug(
-                "Skipping annotation %s: position outside spectrogram. "
+                "Skipping annotation: position outside spectrogram. Pos: %s",
-                "Pos: %s",
-                sound_event_annotation.uuid,
                 (time, frequency),
             )
             continue
@@ -222,20 +134,8 @@ def generate_heatmaps(
         )
         size_heatmap[:, freq_index, time_index] = torch.tensor(size[:])
 
-        # Get the class name of the sound event
+        # If the label is None skip the sound event
-        try:
-            class_name = targets.encode_class(sound_event_annotation)
-        except ValueError as e:
-            logger.warning(
-                "Skipping annotation %s: Unexpected error while encoding "
-                "class name %s",
-                sound_event_annotation.uuid,
-                e,
-            )
-            continue
-
         if class_name is None:
-            # If the label is None skip the sound event
             continue
 
         class_index = targets.class_names.index(class_name)

src/batdetect2/train/logging.py

@@ -1,6 +1,8 @@
+import io
 from typing import Annotated, Any, Literal, Optional, Union
 
-from lightning.pytorch.loggers import Logger
+import numpy as np
+from lightning.pytorch.loggers import Logger, MLFlowLogger, TensorBoardLogger
 from loguru import logger
 from pydantic import Field
 
@@ -140,3 +142,35 @@ def build_logger(config: LoggerConfig) -> Logger:
     creation_func = LOGGER_FACTORY[logger_type]
 
     return creation_func(config)
+
+
+def get_image_plotter(logger: Logger):
+    if isinstance(logger, TensorBoardLogger):
+
+        def plot_figure(name, figure, step):
+            return logger.experiment.add_figure(name, figure, step)
+
+        return plot_figure
+
+    if isinstance(logger, MLFlowLogger):
+
+        def plot_figure(name, figure, step):
+            image = _convert_figure_to_image(figure)
+            return logger.experiment.log_image(
+                run_id=logger.run_id,
+                image=image,
+                key=name,
+                step=step,
+            )
+
+        return plot_figure
+
+
+def _convert_figure_to_image(figure):
+    with io.BytesIO() as buff:
+        figure.savefig(buff, format="raw")
+        buff.seek(0)
+        data = np.frombuffer(buff.getvalue(), dtype=np.uint8)
+    w, h = figure.canvas.get_width_height()
+    im = data.reshape((int(h), int(w), -1))
+    return im

src/batdetect2/train/train.py

@@ -24,9 +24,7 @@ from batdetect2.train.augmentations import (
 from batdetect2.train.callbacks import ValidationMetrics
 from batdetect2.train.clips import build_clipper
 from batdetect2.train.config import FullTrainingConfig, TrainingConfig
-from batdetect2.train.dataset import (
+from batdetect2.train.dataset import TrainingDataset, ValidationDataset
-    TrainingDataset,
-)
 from batdetect2.train.labels import build_clip_labeler
 from batdetect2.train.lightning import TrainingModule
 from batdetect2.train.logging import build_logger
@@ -304,11 +302,11 @@ def build_val_dataset(
     labeller: ClipLabeller,
     preprocessor: PreprocessorProtocol,
     config: Optional[TrainingConfig] = None,
-) -> TrainingDataset:
+) -> ValidationDataset:
     logger.info("Building validation dataset...")
     config = config or TrainingConfig()
 
-    return TrainingDataset(
+    return ValidationDataset(
         clip_annotations,
         audio_loader=audio_loader,
         labeller=labeller,

src/batdetect2/typing/evaluate.py

@@ -11,13 +11,17 @@ __all__ = [
 
 @dataclass
 class MatchEvaluation:
-    match: data.Match
+    clip: data.Clip
 
+    sound_event_annotation: Optional[data.SoundEventAnnotation]
     gt_det: bool
     gt_class: Optional[str]
 
     pred_score: float
     pred_class_scores: Dict[str, float]
+    pred_geometry: Optional[data.Geometry]
+
+    affinity: float
 
     @property
     def pred_class(self) -> Optional[str]:

src/batdetect2/typing/postprocess.py

@@ -77,7 +77,16 @@ class RawPrediction(NamedTuple):
     features: np.ndarray
 
 
-class Detections(NamedTuple):
+class DetectionsArray(NamedTuple):
+    scores: np.ndarray
+    sizes: np.ndarray
+    class_scores: np.ndarray
+    times: np.ndarray
+    frequencies: np.ndarray
+    features: np.ndarray
+
+
+class DetectionsTensor(NamedTuple):
     scores: torch.Tensor
     sizes: torch.Tensor
     class_scores: torch.Tensor
@@ -85,6 +94,16 @@ class Detections(NamedTuple):
     frequencies: torch.Tensor
     features: torch.Tensor
 
+    def numpy(self) -> DetectionsArray:
+        return DetectionsArray(
+            scores=self.scores.detach().numpy(),
+            sizes=self.sizes.detach().numpy(),
+            class_scores=self.class_scores.detach().numpy(),
+            times=self.times.detach().numpy(),
+            frequencies=self.frequencies.detach().numpy(),
+            features=self.features.detach().numpy(),
+        )
+
 
 @dataclass
 class BatDetect2Prediction:
@@ -95,10 +114,10 @@ class BatDetect2Prediction:
 class PostprocessorProtocol(Protocol):
     """Protocol defining the interface for the full postprocessing pipeline."""
 
-    def __call__(self, output: ModelOutput) -> List[Detections]: ...
+    def __call__(self, output: ModelOutput) -> List[DetectionsTensor]: ...
 
     def get_detections(
         self,
         output: ModelOutput,
         clips: Optional[List[data.Clip]] = None,
-    ) -> List[Detections]: ...
+    ) -> List[DetectionsTensor]: ...

src/batdetect2/typing/targets.py

@@ -12,8 +12,8 @@ that components responsible for these tasks can be interacted with consistently
 throughout BatDetect2.
 """
 
-from collections.abc import Callable, Iterable
+from collections.abc import Callable
-from typing import List, Optional, Protocol, Tuple
+from typing import List, Optional, Protocol
 
 import numpy as np
 from soundevent import data