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mbsantiago 2025-01-23 14:08:55 +00:00
parent e9e1f7ce2f
commit f7d6516550
16 changed files with 337 additions and 70 deletions
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7
batdetect2/compat/data.py
View File

@ -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(

56
batdetect2/data.py
View File

@ -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,
audio_dir=info.audio_dir,
info.annotations.path,
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)

2
batdetect2/models/__init__.py
View File

@ -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(

4
batdetect2/models/heads.py
View File

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

14
batdetect2/post_process.py
View File

@ -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,
)
)

2
batdetect2/preprocess/audio.py
View File

@ -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):

3
batdetect2/preprocess/spectrogram.py
View File

@ -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(

73
batdetect2/train/augmentations.py
View File

@ -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, stop - duration)
start_time = np.random.uniform(start, max(stop - duration, start))
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 = ops.adjust_dim_width(example["audio"], "audio_time", len(audio2))
audio1 = example["audio"]
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(

16
batdetect2/train/dataset.py
View File

@ -74,8 +74,20 @@ class LabeledDataset(Dataset):
)
@classmethod
def from_directory(cls, directory: PathLike, extension: str = ".nc"):
return cls(get_files(directory, extension))
def from_directory(
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))

25
batdetect2/train/labels.py
View File

@ -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(
detection_heatmap,
1.0,
time=time,
frequency=frequency,
)
try:
detection_heatmap = arrays.set_value_at_pos(
detection_heatmap,
1.0,
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,

1
batdetect2/train/losses.py
View File

@ -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

36
batdetect2/train/modules.py
View File

@ -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)

14
batdetect2/train/preprocess.py
View File

@ -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)

51
batdetect2/train/targets.py
View File

@ -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(
sound_event_annotation: data.SoundEventAnnotation,
) -> Optional[str]:
tags = set(sound_event_annotation.tags)
replacer = (
build_replacer(replacement_rules) if replacement_rules else lambda x: x
)
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",

66
tests/test_train/test_augmentations.py
View File

@ -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
)

37
uv.lock generated
View File

@ -548,7 +548,7 @@ name = "click"
version = "8.1.7"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "colorama", marker = "platform_system == 'Windows'" },
{ name = "colorama", marker = "sys_platform == 'win32'" },
]
sdist = { url = "https://files.pythonhosted.org/packages/96/d3/f04c7bfcf5c1862a2a5b845c6b2b360488cf47af55dfa79c98f6a6bf98b5/click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de", size = 336121 }
wheels = [
@ -630,6 +630,9 @@ name = "configobj"
version = "5.0.9"
source = { registry = "https://pypi.org/simple" }
sdist = { url = "https://files.pythonhosted.org/packages/f5/c4/c7f9e41bc2e5f8eeae4a08a01c91b2aea3dfab40a3e14b25e87e7db8d501/configobj-5.0.9.tar.gz", hash = "sha256:03c881bbf23aa07bccf1b837005975993c4ab4427ba57f959afdd9d1a2386848", size = 101518 }
wheels = [
{ url = "https://files.pythonhosted.org/packages/a6/c4/0679472c60052c27efa612b4cd3ddd2a23e885dcdc73461781d2c802d39e/configobj-5.0.9-py2.py3-none-any.whl", hash = "sha256:1ba10c5b6ee16229c79a05047aeda2b55eb4e80d7c7d8ecf17ec1ca600c79882", size = 35615 },
]
[[package]]
name = "contourpy"
@ -1433,7 +1436,7 @@ name = "ipykernel"
version = "6.29.5"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "appnope", marker = "platform_system == 'Darwin'" },
{ name = "appnope", marker = "sys_platform == 'darwin'" },
{ name = "comm" },
{ name = "debugpy" },
{ name = "ipython" },
@ -3175,6 +3178,7 @@ wheels = [
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