candock

| English | 中文版 |

A time series signal analysis and classification framework.
It contain multiple network and provide data preprocessing, data augmentation, training, evaluation, testing and other functions.
Some output examples: heatmap running_loss log.txt

Feature

Data preprocessing

• Normliaze : 5_95 | maxmin | None
• Filter : fft | fir | iir | wavelet | None

Data augmentation

Various data augmentation method.
[Time Series Data Augmentation for Deep Learning: A Survey]

• Base : scale, warp, app, aaft, iaaft, filp, crop
• Noise : spike, step, slope, white, pink, blue, brown, violet
• Gan : dcgan

Network

Various networks for evaluation.

1d

lstm, cnn_1d, resnet18_1d, resnet34_1d, multi_scale_resnet_1d, micro_multi_scale_resnet_1d,autoencoder,mlp

2d(stft spectrum)

mobilenet, resnet18, resnet50, resnet101, densenet121, densenet201, squeezenet, dfcnn, multi_scale_resnet,

K-fold

Use k-fold to make the results more reliable. --k_fold&--fold_index

• --k_fold

# fold_num of k-fold. If 0 or 1, no k-fold and cut 80% to train and other to eval.

• --fold_index

"""--fold_index
When --k_fold != 0 or 1:
Cut dataset into sub-set using index , and then run k-fold with sub-set
If input 'auto', it will shuffle dataset and then cut dataset equally
If input: [2,4,6,7]
when len(dataset) == 10
sub-set: dataset[0:2],dataset[2:4],dataset[4:6],dataset[6:7],dataset[7:]
-------
When --k_fold == 0 or 1:
If input 'auto', it will shuffle dataset and then cut 80% dataset to train and other to eval
If input: [5]
when len(dataset) == 10
train-set : dataset[0:5]  eval-set : dataset[5:]
"""

A example: Use EEG to classify sleep stage

sleep-edfx
Thank @swalltail99for the bug. In other to load sleep-edfx dataset，please install mne==0.18.0

pip install mne==0.18.0

Getting Started

Prerequisites

• Linux, Windows,mac
• CPU or NVIDIA GPU + CUDA CuDNN
• Python 3
• Pytroch 1.0+

Dependencies

This code depends on torchvision, numpy, scipy, PyWavelets, matplotlib, tensorboardX， available via pip install.

pip install -r requirements.txt

Clone this repo:

git clone https://github.com/HypoX64/candock
cd candock

Download dataset and pretrained-model

[Google Drive] [百度云,y4ks]

• This datasets consists of signals.npy(shape:18207, 1, 2000) and labels.npy(shape:18207) which can be loaded by "np.load()".
• samples:18207, channel:1, length of each sample:2000, class:50
• Top1 err: 2.09%

Train

• Begin training

python3 train.py --label 50 --input_nc 1 --dataset_dir ./datasets/simple_test --save_dir ./checkpoints/simple_test --model_name micro_multi_scale_resnet_1d --gpu_id 0 --batchsize 64 --k_fold 5
# if you want to use cpu to train, please input --gpu_id -1

• Run "tensorboardx" to see outputs.

tensorboard --logdir checkpoints/tensorboardX --host=[your server's IP]

• Open "http://[your server's IP]:6006" on browser
• More options.

Test

python3 simple_test.py --label 50 --input_nc 1 --model_name micro_multi_scale_resnet_1d --gpu_id 0
# if you want to use cpu to test, please input --gpu_id -1

Training with your own dataset

• step1: Generate signals.npy and labels.npy in the following format.

#1.type:numpydata   signals:np.float32   labels:np.int64
#2.shape  signals:[num,ch,length]    labels:[num]
#num:samples_num, ch :channel_num,  length:length of each sample
#for example:
signals = np.zeros((10,1,10),dtype='np.float64')
labels = np.array([0,0,0,0,0,1,1,1,1,1])      #0->class0    1->class1

• step2: input --dataset_dir "your_dataset_dir" when running code.

More training options.