README.md

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# candock

| English | [中文版](./README_CN.md) |<br><br>
A time series signal analysis and classification framework.<br>
It contain multiple network  and provide data preprocessing, reading, training, evaluation, testing and other functions.<br>
Some output examples: [heatmap](./image/heatmap_eg.png)  [running_loss](./image/running_loss_eg.png)  [log.txt](./docs/log_eg.txt)<br>
Supported network:<br>

>1d
>
>>lstm, cnn_1d, resnet18_1d, resnet34_1d, multi_scale_resnet_1d, micro_multi_scale_resnet_1d


>2d(stft spectrum)
>
>>mobilenet, resnet18, resnet50, resnet101, densenet121, densenet201, squeezenet, dfcnn, multi_scale_resnet,

## A example: Use EEG to classify sleep stage
[sleep-edfx](https://github.com/HypoX64/candock/tree/f24cc44933f494d2235b3bf965a04cde5e6a1ae9)<br>
Thank [@swalltail99](https://github.com/swalltail99)for the bug. In other to load sleep-edfx dataset，please install mne==0.18.0<br>
```bash
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 , matplotlib, available via pip install.<br>
For example:<br>

```bash
pip3 install matplotlib
```
### Clone this repo:
```bash
git clone https://github.com/HypoX64/candock
cd candock
```
### Download dataset and pretrained-model
[[Google Drive]](https://drive.google.com/open?id=1NTtLmT02jqlc81lhtzQ7GlPK8epuHfU5)   [[百度云,y4ks]](https://pan.baidu.com/s/1WKWZL91SekrSlhOoEC1bQA)

* 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
```bash
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](./util/options.py).
### Test
```bash
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.
```python
#1.type:numpydata   signals:np.float64   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.