# -*- coding: utf-8 -*-

'''

@time: 2019/9/8 18:45

@ author: javis

'''

import os

class Config:

    # for data_process.py

    #root = r'D:\ECG'

    root = r'data'

    train_dir = os.path.join(root, 'hf_round1_train/train')

    test_dir = os.path.join(root, 'hf_round1_testA/testA')

    train_label = os.path.join(root, 'hf_round1_label.txt')

    test_label = os.path.join(root, 'hf_round1_subA.txt')

    arrythmia = os.path.join(root, 'hf_round1_arrythmia.txt')

    train_data = os.path.join(root, 'train.pth')

    # for train

    #训练的模型名称

    model_name = 'resnet34'

    #在第几个epoch进行到下一个state,调整lr

    stage_epoch = [32,64,128]

    #训练时的batch大小

    batch_size = 64

    #label的类别数

    num_classes = 55

    #最大训练多少个epoch

    max_epoch = 256

    #目标的采样长度

    target_point_num = 2048

    #保存模型的文件夹

    ckpt = 'ckpt'

    #保存提交文件的文件夹

    sub_dir = 'submit'

    #初始的学习率

    lr = 1e-3

    #保存模型当前epoch的权重

    current_w = 'current_w.pth'

    #保存最佳的权重

    best_w = 'best_w.pth'

    # 学习率衰减 lr/=lr_decay

    lr_decay = 10

    #for test

    temp_dir=os.path.join(root,'temp')

config = Config()