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, pywt, matplotlib, available via pip install.
For example:
pip install matplotlib
Clone this repo:
git clone https://github.com/HypoX64/candock
cd candock
Download dataset and pretrained-model
- 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
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
- 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.