Source code for torcheeg.datasets.module.numpy_dataset
import numpy as np
from typing import Callable, Dict, Tuple, Union
from ..functional.numpy import numpy_constructor
from .base_dataset import BaseDataset
[docs]class NumpyDataset(BaseDataset):
r'''
A general dataset, this class converts EEG signals and annotations in Numpy format into dataset types, and caches the generated results in a unified input and output format (IO).
A tiny case shows the use of :obj:`NumpyDataset`:
.. code-block:: python
# Mock 100 EEG samples. Each EEG signal contains a signal of length 1 s at a sampling rate of 128 sampled by 32 electrodes.
X = np.random.randn(100, 32, 128)
# Mock 100 labels, denoting valence and arousal of subjects during EEG recording.
y = {
'valence': np.random.randint(10, size=100),
'arousal': np.random.randint(10, size=100)
}
dataset = NumpyDataset(X=X,
y=y,
io_path=io_path,
offline_transform=transforms.Compose(
[transforms.BandDifferentialEntropy()]),
online_transform=transforms.ToTensor(),
label_transform=transforms.Compose([
transforms.Select('valence'),
transforms.Binary(5.0),
]),
num_worker=2,
num_samples_per_worker=50)
print(dataset[0])
# EEG signal (torch.Tensor[32, 4]),
# coresponding baseline signal (torch.Tensor[32, 4]),
# label (int)
In particular, TorchEEG utilizes the producer-consumer model to allow multi-process data preprocessing. If your data preprocessing is time consuming, consider increasing :obj:`num_worker` for higher speedup. If running under Windows, please use the proper idiom in the main module:
.. code-block:: python
if __name__ == '__main__':
# Mock 100 EEG samples. Each EEG signal contains a signal of length 1 s at a sampling rate of 128 sampled by 32 electrodes.
X = np.random.randn(100, 32, 128)
# Mock 100 labels, denoting valence and arousal of subjects during EEG recording.
y = {
'valence': np.random.randint(10, size=100),
'arousal': np.random.randint(10, size=100)
}
dataset = NumpyDataset(X=X,
y=y,
io_path=io_path,
offline_transform=transforms.Compose(
[transforms.BandDifferentialEntropy()]),
online_transform=transforms.ToTensor(),
label_transform=transforms.Compose([
transforms.Select('valence'),
transforms.Binary(5.0),
]),
num_worker=2,
num_samples_per_worker=50)
print(dataset[0])
# EEG signal (torch.Tensor[32, 4]),
# coresponding baseline signal (torch.Tensor[32, 4]),
# label (int)
Args:
X (np.ndarray): An array in :obj:`numpy.ndarray` format representing the EEG signal samples in the dataset. The shape of the array is :obj:`[num_sample, ...]` where :obj:`num_sample` is the number of samples.
y (dict): A dictionary that records the labels corresponding to EEG samples. The keys of the dictionary represent the names of the labels, and the values are lists of labels whose length is consistent with the EEG signal samples.
online_transform (Callable, optional): The transformation of the EEG signals and baseline EEG signals. The input is a :obj:`np.ndarray`, and the ouput is used as the first and second value of each element in the dataset. (default: :obj:`None`)
offline_transform (Callable, optional): The usage is the same as :obj:`online_transform`, but executed before generating IO intermediate results. (default: :obj:`None`)
label_transform (Callable, optional): The transformation of the label. The input is an information dictionary, and the ouput is used as the third value of each element in the dataset. (default: :obj:`None`)
before_trial (Callable, optional): The hook performed on the trial to which the sample belongs. It is performed before the offline transformation and thus typically used to implement context-dependent sample transformations, such as moving averages, etc. The input and output of this hook function should be a :obj:`np.ndarray`, whose shape is (number of EEG samples per trial, ...).
after_trial (Callable, optional): The hook performed on the trial to which the sample belongs. It is performed after the offline transformation and thus typically used to implement context-dependent sample transformations, such as moving averages, etc. The input and output of this hook function should be a sequence of dictionaries representing a sequence of EEG samples. Each dictionary contains two key-value pairs, indexed by :obj:`eeg` (the EEG signal matrix) and :obj:`key` (the index in the database) respectively
io_path (str): The path to generated unified data IO, cached as an intermediate result. (default: :obj:`./io/deap`)
io_size (int): Maximum size database may grow to; used to size the memory mapping. If database grows larger than ``map_size``, an exception will be raised and the user must close and reopen. (default: :obj:`10485760`)
io_mode (str): Storage mode of EEG signal. When io_mode is set to :obj:`lmdb`, TorchEEG provides an efficient database (LMDB) for storing EEG signals. LMDB may not perform well on limited operating systems, where a file system based EEG signal storage is also provided. When io_mode is set to :obj:`pickle`, pickle-based persistence files are used. (default: :obj:`lmdb`)
num_worker (int): How many subprocesses to use for data processing. (default: :obj:`0`)
num_samples_per_worker (int): The number of samples processed by each process. Once the specified number of samples are processed, the process will be destroyed and new processes will be created to perform new tasks. (default: :obj:`100`)
verbose (bool): Whether to display logs during processing, such as progress bars, etc. (default: :obj:`True`)
in_memory (bool): Whether to load the entire dataset into memory. If :obj:`in_memory` is set to True, then the first time an EEG sample is read, the entire dataset is loaded into memory for subsequent retrieval. Otherwise, the dataset is stored on disk to avoid the out-of-memory problem. (default: :obj:`False`)
'''
def __init__(self,
X: np.ndarray,
y: Dict,
online_transform: Union[None, Callable] = None,
offline_transform: Union[None, Callable] = None,
label_transform: Union[None, Callable] = None,
before_trial: Union[None, Callable] = None,
after_trial: Union[Callable, None] = None,
io_path: str = './io/numpy',
io_size: int = 10485760,
io_mode: str = 'lmdb',
num_worker: int = 0,
num_samples_per_worker: int = 100,
verbose: bool = True,
in_memory: bool = False):
numpy_constructor(X=X,
y=y,
before_trial=before_trial,
transform=offline_transform,
after_trial=after_trial,
io_path=io_path,
io_size=io_size,
io_mode=io_mode,
num_worker=num_worker,
num_samples_per_worker=num_samples_per_worker,
verbose=verbose)
super().__init__(io_path=io_path,
io_size=io_size,
io_mode=io_mode,
in_memory=in_memory)
self.online_transform = online_transform
self.offline_transform = offline_transform
self.label_transform = label_transform
self.before_trial = before_trial
self.after_trial = after_trial
self.num_worker = num_worker
self.num_samples_per_worker = num_samples_per_worker
self.verbose = verbose
def __getitem__(self, index: int) -> Tuple:
info = self.read_info(index)
eeg_index = str(info['clip_id'])
eeg = self.read_eeg(eeg_index)
signal = eeg
label = info
if self.online_transform:
signal = self.online_transform(eeg=eeg)['eeg']
if self.label_transform:
label = self.label_transform(y=info)['y']
return signal, label
@property
def repr_body(self) -> Dict:
return dict(
super().repr_body, **{
'online_transform': self.online_transform,
'offline_transform': self.offline_transform,
'label_transform': self.label_transform,
'before_trial': self.before_trial,
'after_trial': self.after_trial,
'num_worker': self.num_worker,
'num_samples_per_worker': self.num_samples_per_worker,
'verbose': self.verbose,
'io_size': self.io_size
})
