mirror of
https://github.com/macaodha/batdetect2.git
synced 2025-06-29 14:41:58 +02:00
104 lines
3.0 KiB
Python
104 lines
3.0 KiB
Python
from pathlib import Path
|
|
from typing import List, Optional, Sequence, Tuple
|
|
|
|
import numpy as np
|
|
import torch
|
|
import xarray as xr
|
|
from soundevent import data
|
|
from torch.utils.data import Dataset
|
|
|
|
from batdetect2.train.augmentations import Augmentation
|
|
from batdetect2.train.types import ClipperProtocol, TrainExample
|
|
|
|
__all__ = [
|
|
"LabeledDataset",
|
|
]
|
|
|
|
|
|
class LabeledDataset(Dataset):
|
|
def __init__(
|
|
self,
|
|
filenames: Sequence[data.PathLike],
|
|
clipper: ClipperProtocol,
|
|
augmentation: Optional[Augmentation] = None,
|
|
):
|
|
self.filenames = filenames
|
|
self.clipper = clipper
|
|
self.augmentation = augmentation
|
|
|
|
def __len__(self):
|
|
return len(self.filenames)
|
|
|
|
def __getitem__(self, idx) -> TrainExample:
|
|
dataset = self.get_dataset(idx)
|
|
dataset, start_time, end_time = self.clipper.extract_clip(dataset)
|
|
|
|
if self.augmentation:
|
|
dataset = self.augmentation(dataset)
|
|
|
|
return TrainExample(
|
|
spec=self.to_tensor(dataset["spectrogram"]).unsqueeze(0),
|
|
detection_heatmap=self.to_tensor(dataset["detection"]),
|
|
class_heatmap=self.to_tensor(dataset["class"]),
|
|
size_heatmap=self.to_tensor(dataset["size"]),
|
|
idx=torch.tensor(idx),
|
|
start_time=start_time,
|
|
end_time=end_time,
|
|
)
|
|
|
|
@classmethod
|
|
def from_directory(
|
|
cls,
|
|
directory: data.PathLike,
|
|
clipper: ClipperProtocol,
|
|
extension: str = ".nc",
|
|
augmentation: Optional[Augmentation] = None,
|
|
):
|
|
return cls(
|
|
filenames=list_preprocessed_files(directory, extension),
|
|
clipper=clipper,
|
|
augmentation=augmentation,
|
|
)
|
|
|
|
def get_random_example(self) -> Tuple[xr.Dataset, float, float]:
|
|
idx = np.random.randint(0, len(self))
|
|
dataset = self.get_dataset(idx)
|
|
|
|
dataset, start_time, end_time = self.clipper.extract_clip(dataset)
|
|
|
|
return dataset, start_time, end_time
|
|
|
|
def get_dataset(self, idx) -> xr.Dataset:
|
|
return xr.open_dataset(self.filenames[idx])
|
|
|
|
def get_clip_annotation(self, idx) -> data.ClipAnnotation:
|
|
return data.ClipAnnotation.model_validate_json(
|
|
self.get_dataset(idx).attrs["clip_annotation"]
|
|
)
|
|
|
|
def to_tensor(
|
|
self,
|
|
array: xr.DataArray,
|
|
dtype=np.float32,
|
|
) -> torch.Tensor:
|
|
return torch.tensor(array.values.astype(dtype))
|
|
|
|
|
|
def list_preprocessed_files(
|
|
directory: data.PathLike, extension: str = ".nc"
|
|
) -> Sequence[Path]:
|
|
return list(Path(directory).glob(f"*{extension}"))
|
|
|
|
|
|
class RandomExampleSource:
|
|
def __init__(self, filenames: List[str], clipper: ClipperProtocol):
|
|
self.filenames = filenames
|
|
self.clipper = clipper
|
|
|
|
def __call__(self):
|
|
index = int(np.random.randint(len(self.filenames)))
|
|
filename = self.filenames[index]
|
|
dataset = xr.open_dataset(filename)
|
|
example, _, _ = self.clipper.extract_clip(dataset)
|
|
return example
|