WIP

batdetect2/compat/data.py

@@ -215,6 +215,7 @@ def annotation_to_sound_event(
 def file_annotation_to_clip(
     file_annotation: FileAnnotation,
     audio_dir: Optional[PathLike] = None,
+    label_key: str = "class",
 ) -> data.Clip:
     """Convert file annotation to recording."""
     audio_dir = audio_dir or Path.cwd()
@@ -227,6 +228,12 @@ def file_annotation_to_clip(
     recording = data.Recording.from_file(
         full_path,
         time_expansion=file_annotation.time_exp,
+        tags=[
+            data.Tag(
+                term=data.term_from_key(label_key),
+                value=file_annotation.label,
+            )
+        ],
     )
 
     return data.Clip(

batdetect2/data.py

@@ -1,5 +1,5 @@
 from pathlib import Path
-from typing import List, Literal, Tuple, Union
+from typing import List, Literal, Optional, Tuple, Union
 
 from pydantic import Field
 from soundevent import data, io
@@ -8,7 +8,11 @@ from batdetect2.compat.data import (
     load_annotation_project_from_dir,
     load_annotation_project_from_file,
 )
-from batdetect2.configs import BaseConfig
+from batdetect2.configs import BaseConfig, load_config
+
+__all__ = [
+    "load_datasets_from_config",
+]
 
 
 class BatDetect2AnnotationFiles(BaseConfig):
@@ -23,7 +27,7 @@ class BatDetect2AnnotationFile(BaseConfig):
 
 class AOEFAnnotationFile(BaseConfig):
     format: Literal["aoef"] = "aoef"
-    annotations_file: Path
+    path: Path
 
 
 AnnotationFormats = Union[
@@ -44,25 +48,39 @@ class DatasetsConfig(BaseConfig):
     test: List[DatasetInfo] = Field(default_factory=list)
 
 
-def load_dataset(info: DatasetInfo) -> data.AnnotationProject:
+def load_dataset(
+    info: DatasetInfo,
+    audio_dir: Optional[Path] = None,
+    base_dir: Optional[Path] = None,
+) -> data.AnnotationProject:
+    audio_dir = (
+        info.audio_dir if base_dir is None else base_dir / info.audio_dir
+    )
+
+    path = (
+        info.annotations.path
+        if base_dir is None
+        else base_dir / info.annotations.path
+    )
+
     if info.annotations.format == "batdetect2":
         return load_annotation_project_from_dir(
-            info.annotations.path,
+            path,
             name=info.name,
-            audio_dir=info.audio_dir,
+            audio_dir=audio_dir,
         )
 
     if info.annotations.format == "batdetect2_file":
         return load_annotation_project_from_file(
-            info.annotations.path,
+            path,
             name=info.name,
-            audio_dir=info.audio_dir,
+            audio_dir=audio_dir,
         )
 
     if info.annotations.format == "aoef":
         return io.load(  # type: ignore
-            info.annotations.annotations_file,
+            info.annotations.path,
-            audio_dir=info.audio_dir,
+            audio_dir=audio_dir,
         )
 
     raise NotImplementedError(
@@ -72,16 +90,30 @@ def load_dataset(info: DatasetInfo) -> data.AnnotationProject:
 
 def load_datasets(
     config: DatasetsConfig,
+    base_dir: Optional[Path] = None,
 ) -> Tuple[List[data.ClipAnnotation], List[data.ClipAnnotation]]:
     test_annotations = []
     train_annotations = []
 
     for dataset in config.train:
-        project = load_dataset(dataset)
+        project = load_dataset(dataset, base_dir=base_dir)
         train_annotations.extend(project.clip_annotations)
 
     for dataset in config.test:
-        project = load_dataset(dataset)
+        project = load_dataset(dataset, base_dir=base_dir)
         test_annotations.extend(project.clip_annotations)
 
     return train_annotations, test_annotations
+
+
+def load_datasets_from_config(
+    path: data.PathLike,
+    field: Optional[str] = None,
+    base_dir: Optional[Path] = None,
+):
+    config = load_config(
+        path=path,
+        schema=DatasetsConfig,
+        field=field,
+    )
+    return load_datasets(config, base_dir=base_dir)

batdetect2/models/__init__.py

@@ -28,7 +28,7 @@ class ModelType(str, Enum):
 
 class ModelConfig(BaseConfig):
     name: ModelType = ModelType.Net2DFast
-    num_features: int = 128
+    num_features: int = 32
 
 
 def get_backbone(

batdetect2/models/heads.py

@@ -41,8 +41,8 @@ class BBoxHead(nn.Module):
         self.in_channels = in_channels
 
         self.bbox = nn.Conv2d(
-            self.feature_extractor.out_channels,
+            in_channels=self.in_channels,
-            2,
+            out_channels=2,
             kernel_size=1,
             padding=0,
         )

batdetect2/post_process.py

@@ -1,6 +1,6 @@
 """Module for postprocessing model outputs."""
 
-from typing import Callable, List, Tuple, Union
+from typing import Callable, List, Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -30,15 +30,12 @@ class PostprocessConfig(BaseModel):
     top_k_per_sec: int = Field(default=TOP_K_PER_SEC, gt=0)
 
 
-TagFunction = Callable[[int], List[data.Tag]]
-
-
 def postprocess_model_outputs(
     outputs: ModelOutput,
     clips: List[data.Clip],
     classes: List[str],
     decoder: Callable[[str], List[data.Tag]],
-    config: PostprocessConfig,
+    config: Optional[PostprocessConfig] = None,
 ) -> List[data.ClipPrediction]:
     """Postprocesses model outputs to generate clip predictions.
 
@@ -68,6 +65,9 @@ def postprocess_model_outputs(
     ValueError
         If the number of predictions does not match the number of clips.
     """
+
+    config = config or PostprocessConfig()
+
     num_predictions = len(outputs.detection_probs)
 
     if num_predictions == 0:
@@ -189,7 +189,7 @@ def compute_sound_events_from_outputs(
                 [
                     data.PredictedTag(
                         tag=tag,
-                        score=class_score.item(),
+                        score=max(min(class_score.item(), 1), 0),
                     )
                     for tag in corresponding_tags
                 ]
@@ -220,7 +220,7 @@ def compute_sound_events_from_outputs(
         predictions.append(
             data.SoundEventPrediction(
                 sound_event=sound_event,
-                score=score.item(),
+                score=max(min(score.item(), 1), 0),
                 tags=predicted_tags,
             )
         )

batdetect2/preprocess/audio.py

@@ -12,7 +12,7 @@ from batdetect2.configs import BaseConfig
 
 TARGET_SAMPLERATE_HZ = 256_000
 SCALE_RAW_AUDIO = False
-DEFAULT_DURATION = 1
+DEFAULT_DURATION = None
 
 
 class ResampleConfig(BaseConfig):

batdetect2/preprocess/spectrogram.py

@@ -71,11 +71,12 @@ def compute_spectrogram(
     if config.size.divide_factor:
         # Need to pad the audio to make sure the spectrogram has a
         # width compatible with the divide factor
+        resize_factor = config.size.resize_factor or 1
         wav = pad_audio(
             wav,
             window_duration=config.fft.window_duration,
             window_overlap=config.fft.window_overlap,
-            divide_factor=config.size.divide_factor,
+            divide_factor=int(config.size.divide_factor / resize_factor),
         )
 
     spec = stft(

batdetect2/train/augmentations.py

@@ -20,6 +20,37 @@ class AugmentationConfig(BaseConfig):
 class SubclipConfig(BaseConfig):
     enable: bool = True
     duration: Optional[float] = None
+    width: Optional[int] = 512
+
+
+def adjust_dataset_width(
+    example: xr.Dataset,
+    duration: Optional[float] = None,
+    width: Optional[int] = None,
+) -> xr.Dataset:
+    step = arrays.get_dim_step(example, "time")  # type: ignore
+
+    if width is None:
+        if duration is None:
+            raise ValueError("Either duration or width must be provided")
+
+        width = int(np.floor(duration / step))
+
+    adjusted_arrays = {
+        name: ops.adjust_dim_width(array, "time", width)
+        for name, array in example.items()
+        if name != "audio"
+    }
+
+    ratio = width / example.spectrogram.sizes["time"]
+    audio_width = int(example.audio.sizes["audio_time"] * ratio)
+    adjusted_arrays["audio"] = ops.adjust_dim_width(
+        example["audio"],
+        "audio_time",
+        audio_width,
+    )
+
+    return xr.Dataset(data_vars=adjusted_arrays)
 
 
 def select_random_subclip(
@@ -30,6 +61,7 @@ def select_random_subclip(
 ) -> xr.Dataset:
     """Select a random subclip from a clip."""
     step = arrays.get_dim_step(example, "time")  # type: ignore
+    start, stop = arrays.get_dim_range(example, "time")  # type: ignore
 
     if width is None:
         if duration is None:
@@ -41,15 +73,19 @@ def select_random_subclip(
         duration = width * step
 
     if start_time is None:
-        start, stop = arrays.get_dim_range(example, "time")  # type: ignore
+        start_time = np.random.uniform(start, max(stop - duration, start))
-        start_time = np.random.uniform(start, stop - duration)
+
+    if start_time + duration > stop:
+        example = adjust_dataset_width(example, width=width)
 
     start_index = arrays.get_coord_index(
         example,  # type: ignore
         "time",
         start_time,
     )
+
     end_index = start_index + width - 1
+
     start_time = example.time.values[start_index]
     end_time = example.time.values[end_index]
 
@@ -78,8 +114,15 @@ def mix_examples(
     if weight is None:
         weight = np.random.uniform(min_weight, max_weight)
 
-    audio2 = other["audio"].values
+    audio1 = example["audio"]
-    audio1 = ops.adjust_dim_width(example["audio"], "audio_time", len(audio2))
+
+    audio2 = ops.adjust_dim_width(
+        other["audio"], "audio_time", len(audio1)
+    ).values
+
+    if len(audio2) > len(audio1):
+        audio2 = audio2[: len(audio1)]
+
     combined = weight * audio1 + (1 - weight) * audio2
 
     spec = compute_spectrogram(
@@ -104,11 +147,16 @@ def mix_examples(
     return xr.Dataset(
         {
             "audio": combined,
-            "spectrogram": spec,
+            "spectrogram": xr.DataArray(
+                data=spec.data,
+                dims=example["spectrogram"].dims,
+                coords=example["spectrogram"].coords,
+            ),
             "detection": detection_heatmap,
             "class": class_heatmap,
             "size": size_heatmap,
-        }
+        },
+        attrs=example.attrs,
     )
 
 
@@ -146,7 +194,14 @@ def add_echo(
         config=config.spectrogram,
     )
 
-    return example.assign(audio=audio, spectrogram=spectrogram)
+    return example.assign(
+        audio=audio,
+        spectrogram=xr.DataArray(
+            data=spectrogram.data,
+            dims=example["spectrogram"].dims,
+            coords=example["spectrogram"].coords,
+        ),
+    )
 
 
 class VolumeAugmentationConfig(AugmentationConfig):
@@ -340,15 +395,17 @@ def augment_example(
         example = select_random_subclip(
             example,
             duration=config.subclip.duration,
+            width=config.subclip.width,
         )
 
-    if should_apply(config.mix) and others is not None:
+    if should_apply(config.mix) and (others is not None):
         other = others()
 
         if config.subclip.enable:
             other = select_random_subclip(
                 other,
                 duration=config.subclip.duration,
+                width=config.subclip.width,
             )
 
         example = mix_examples(

batdetect2/train/dataset.py

@@ -74,8 +74,20 @@ class LabeledDataset(Dataset):
         )
 
     @classmethod
-    def from_directory(cls, directory: PathLike, extension: str = ".nc"):
+    def from_directory(
-        return cls(get_files(directory, extension))
+        cls,
+        directory: PathLike,
+        extension: str = ".nc",
+        augment: bool = False,
+        preprocessing_config: Optional[PreprocessingConfig] = None,
+        augmentation_config: Optional[AugmentationsConfig] = None,
+    ):
+        return cls(
+            get_files(directory, extension),
+            augment=augment,
+            preprocessing_config=preprocessing_config,
+            augmentation_config=augmentation_config,
+        )
 
     def get_random_example(self) -> xr.Dataset:
         idx = np.random.randint(0, len(self))

batdetect2/train/labels.py

@@ -1,3 +1,4 @@
+from collections.abc import Iterable
 from typing import Callable, List, Optional, Sequence, Tuple
 
 import numpy as np
@@ -22,7 +23,7 @@ def generate_heatmaps(
     sound_events: Sequence[data.SoundEventAnnotation],
     spec: xr.DataArray,
     class_names: List[str],
-    encoder: Callable[[data.SoundEventAnnotation], Optional[str]],
+    encoder: Callable[[Iterable[data.Tag]], Optional[str]],
     target_sigma: float = 3.0,
     position: Positions = "bottom-left",
     time_scale: float = 1000.0,
@@ -64,23 +65,29 @@ def generate_heatmaps(
         time, frequency = geometry.get_geometry_point(geom, position=position)
 
         # Set 1.0 at the position of the sound event in the detection heatmap
-        detection_heatmap = arrays.set_value_at_pos(
+        try:
-            detection_heatmap,
+            detection_heatmap = arrays.set_value_at_pos(
-            1.0,
+                detection_heatmap,
-            time=time,
+                1.0,
-            frequency=frequency,
+                time=time,
-        )
+                frequency=frequency,
+            )
+        except KeyError:
+            # Skip the sound event if the position is outside the spectrogram
+            continue
 
         # Set the size of the sound event at the position in the size heatmap
         start_time, low_freq, end_time, high_freq = geometry.compute_bounds(
             geom
         )
+
         size = np.array(
             [
                 (end_time - start_time) * time_scale,
                 (high_freq - low_freq) * frequency_scale,
             ]
         )
+
         size_heatmap = arrays.set_value_at_pos(
             size_heatmap,
             size,
@@ -89,14 +96,12 @@ def generate_heatmaps(
         )
 
         # Get the class name of the sound event
-        class_name = encoder(sound_event_annotation)
+        class_name = encoder(sound_event_annotation.tags)
 
         if class_name is None:
             # If the label is None skip the sound event
             continue
 
-        # Set 1.0 at the position and category of the sound event in the class
-        # heatmap
         class_heatmap = arrays.set_value_at_pos(
             class_heatmap,
             1.0,

batdetect2/train/losses.py

@@ -6,6 +6,7 @@ from pydantic import Field
 
 from batdetect2.configs import BaseConfig
 from batdetect2.models.typing import ModelOutput
+from batdetect2.plot import detection
 from batdetect2.train.dataset import TrainExample
 
 

batdetect2/train/modules.py

@@ -13,6 +13,7 @@ from batdetect2.models import (
     get_backbone,
 )
 from batdetect2.models.typing import ModelOutput
+from batdetect2.post_process import PostprocessConfig
 from batdetect2.preprocess import PreprocessingConfig
 from batdetect2.train.dataset import TrainExample
 from batdetect2.train.losses import LossConfig, compute_loss
@@ -37,6 +38,9 @@ class ModuleConfig(BaseConfig):
     preprocessing: PreprocessingConfig = Field(
         default_factory=PreprocessingConfig
     )
+    postprocessing: PostprocessConfig = Field(
+        default_factory=PostprocessConfig
+    )
 
 
 class DetectorModel(L.LightningModule):
@@ -50,8 +54,9 @@ class DetectorModel(L.LightningModule):
         self.config = config or ModuleConfig()
         self.save_hyperparameters()
 
+        size = self.config.preprocessing.spectrogram.size
         self.backbone = get_backbone(
-            input_height=self.config.preprocessing.spectrogram.size.height,
+            input_height=int(size.height * (size.resize_factor or 1)),
             config=self.config.backbone,
         )
 
@@ -62,11 +67,13 @@ class DetectorModel(L.LightningModule):
 
         self.bbox = BBoxHead(in_channels=self.backbone.out_channels)
 
-        conf = self.training_config.loss.classification
+        conf = self.config.train.loss.classification
         self.class_weights = (
             torch.tensor(conf.class_weights) if conf.class_weights else None
         )
 
+        self.validation_predictions = []
+
     def forward(self, spec: torch.Tensor) -> ModelOutput:  # type: ignore
         features = self.backbone(spec)
         detection_probs, classification_probs = self.classifier(features)
@@ -86,8 +93,33 @@ class DetectorModel(L.LightningModule):
             conf=self.config.train.loss,
             class_weights=self.class_weights,
         )
+
+        self.log("train/loss/total", losses.total, prog_bar=True, logger=True)
+        self.log("train/loss/detection", losses.total, logger=True)
+        self.log("train/loss/size", losses.total, logger=True)
+        self.log("train/loss/classification", losses.total, logger=True)
+
         return losses.total
 
+    def validation_step(self, batch: TrainExample, batch_idx: int) -> None:
+        outputs = self.forward(batch.spec)
+
+        losses = compute_loss(
+            batch,
+            outputs,
+            conf=self.config.train.loss,
+            class_weights=self.class_weights,
+        )
+
+        self.log("val/loss/total", losses.total, prog_bar=True, logger=True)
+        self.log("val/loss/detection", losses.total, logger=True)
+        self.log("val/loss/size", losses.total, logger=True)
+        self.log("val/loss/classification", losses.total, logger=True)
+
+        dataloaders = self.trainer.val_dataloaders
+        print(dataloaders)
+
+
     def configure_optimizers(self):
         conf = self.config.train.optimizer
         optimizer = optim.Adam(self.parameters(), lr=conf.learning_rate)

batdetect2/train/preprocess.py

@@ -62,12 +62,16 @@ def generate_train_example(
         include=config.target.include,
         exclude=config.target.exclude,
     )
+
     selected_events = [
         event for event in clip_annotation.sound_events if filter_fn(event)
     ]
 
+    encoder = build_encoder(
+        config.target.classes,
+        replacement_rules=config.target.replace,
+    )
     class_names = get_class_names(config.target.classes)
-    encoder = build_encoder(config.target.classes)
 
     detection_heatmap, class_heatmap, size_heatmap = generate_heatmaps(
         selected_events,
@@ -172,5 +176,11 @@ def preprocess_single_annotation(
     if path.is_file() and replace:
         path.unlink()
 
-    sample = generate_train_example(clip_annotation, config=config)
+    try:
+        sample = generate_train_example(clip_annotation, config=config)
+    except Exception as error:
+        raise RuntimeError(
+            f"Failed to process annotation: {clip_annotation.uuid}"
+        ) from error
+
     save_to_file(sample, path)

batdetect2/train/targets.py

@@ -1,13 +1,22 @@
+from collections.abc import Iterable
 from functools import partial
+from pathlib import Path
 from typing import Callable, List, Optional, Set
 
 from pydantic import Field
 from soundevent import data
 
-from batdetect2.configs import BaseConfig
+from batdetect2.configs import BaseConfig, load_config
 from batdetect2.terms import TagInfo, get_tag_from_info
 
 
+class ReplaceConfig(BaseConfig):
+    """Configuration for replacing tags."""
+
+    original: TagInfo
+    replacement: TagInfo
+
+
 class TargetConfig(BaseConfig):
     """Configuration for target generation."""
 
@@ -23,6 +32,7 @@ class TargetConfig(BaseConfig):
     include: Optional[List[TagInfo]] = Field(
         default_factory=lambda: [TagInfo(key="event", value="Echolocation")]
     )
+
     exclude: Optional[List[TagInfo]] = Field(
         default_factory=lambda: [
             TagInfo(key="class", value=""),
@@ -31,6 +41,8 @@ class TargetConfig(BaseConfig):
         ]
     )
 
+    replace: Optional[List[ReplaceConfig]] = None
+
 
 def build_sound_event_filter(
     include: Optional[List[TagInfo]] = None,
@@ -57,9 +69,24 @@ def get_class_names(classes: List[TagInfo]) -> List[str]:
     return sorted({get_tag_label(tag) for tag in classes})
 
 
+def build_replacer(
+    rules: List[ReplaceConfig],
+) -> Callable[[data.Tag], data.Tag]:
+    mapping = {
+        get_tag_from_info(rule.original): get_tag_from_info(rule.replacement)
+        for rule in rules
+    }
+
+    def replacer(tag: data.Tag) -> data.Tag:
+        return mapping.get(tag, tag)
+
+    return replacer
+
+
 def build_encoder(
     classes: List[TagInfo],
-) -> Callable[[data.SoundEventAnnotation], Optional[str]]:
+    replacement_rules: Optional[List[ReplaceConfig]] = None,
+) -> Callable[[Iterable[data.Tag]], Optional[str]]:
     target_tags = set([get_tag_from_info(tag) for tag in classes])
 
     tag_mapping = {
@@ -67,12 +94,16 @@ def build_encoder(
         for tag, tag_info in zip(target_tags, classes)
     }
 
-    def encoder(
+    replacer = (
-        sound_event_annotation: data.SoundEventAnnotation,
+        build_replacer(replacement_rules) if replacement_rules else lambda x: x
-    ) -> Optional[str]:
+    )
-        tags = set(sound_event_annotation.tags)
 
-        intersection = tags & target_tags
+    def encoder(
+        tags: Iterable[data.Tag],
+    ) -> Optional[str]:
+        sanitized_tags = {replacer(tag) for tag in tags}
+
+        intersection = sanitized_tags & target_tags
 
         if not intersection:
             return None
@@ -115,6 +146,12 @@ def filter_sound_event(
     return True
 
 
+def load_target_config(
+    path: Path, field: Optional[str] = None
+) -> TargetConfig:
+    return load_config(path, schema=TargetConfig, field=field)
+
+
 DEFAULT_SPECIES_LIST = [
     "Barbastellus barbastellus",
     "Eptesicus serotinus",

tests/test_train/test_augmentations.py

@@ -1,10 +1,12 @@
 from collections.abc import Callable
 
+import numpy as np
 import xarray as xr
 from soundevent import data
 
 from batdetect2.train.augmentations import (
     add_echo,
+    adjust_dataset_width,
     mix_examples,
     select_random_subclip,
 )
@@ -68,3 +70,67 @@ def test_selected_random_subclip_has_the_correct_width(
     subclip = select_random_subclip(original, width=100)
 
     assert subclip["spectrogram"].shape[1] == 100
+
+
+def test_adjust_dataset_width():
+    height = 128
+    width = 512
+    samplerate = 48_000
+
+    times = np.linspace(0, 1, width)
+
+    audio_times = np.linspace(0, 1, samplerate)
+    frequency = np.linspace(0, 24_000, height)
+
+    width_subset = 356
+    audio_width_subset = int(samplerate * width_subset / width)
+
+    times_subset = times[:width_subset]
+    audio_times_subset = audio_times[:audio_width_subset]
+    dimensions = ["width", "height"]
+    class_names = [f"species_{i}" for i in range(17)]
+
+    spectrogram = np.random.random([height, width_subset])
+    sizes = np.random.random([len(dimensions), height, width_subset])
+    classes = np.random.random([len(class_names), height, width_subset])
+    audio = np.random.random([int(samplerate * width_subset / width)])
+
+    dataset = xr.Dataset(
+        data_vars={
+            "audio": (("audio_time",), audio),
+            "spectrogram": (("frequency", "time"), spectrogram),
+            "sizes": (("dimension", "frequency", "time"), sizes),
+            "classes": (("class", "frequency", "time"), classes),
+        },
+        coords={
+            "audio_time": audio_times_subset,
+            "time": times_subset,
+            "frequency": frequency,
+            "dimension": dimensions,
+            "class": class_names,
+        },
+    )
+
+    adjusted = adjust_dataset_width(dataset, width=width)
+
+    # Spectrogram was adjusted correctly
+    assert np.isclose(adjusted["spectrogram"].time, times).all()
+    assert (adjusted["spectrogram"].frequency == frequency).all()
+
+    # Sizes was adjusted correctly
+    assert np.isclose(adjusted["sizes"].time, times).all()
+    assert (adjusted["sizes"].frequency == frequency).all()
+    assert list(adjusted["sizes"].dimension.values) == dimensions
+
+    # Sizes was adjusted correctly
+    assert np.isclose(adjusted["classes"].time, times).all()
+    assert (adjusted["sizes"].frequency == frequency).all()
+    assert list(adjusted["classes"]["class"].values) == class_names
+
+    # Audio time was adjusted corretly
+    assert np.isclose(
+        len(adjusted["audio"].audio_time), len(audio_times), atol=2
+    )
+    assert np.isclose(
+        adjusted["audio"].audio_time[-1], audio_times[-1], atol=1e-3
+    )

uv.lock

@@ -548,7 +548,7 @@ name = "click"
 version = "8.1.7"
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+    { name = "appnope", marker = "sys_platform == 'darwin'" },
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@@ -3632,19 +3639,19 @@ dependencies = [
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