import os
import time
import shutil
import random
import torch
import numpy as np
import sys
sys.path.append("..")
from util import util, transformer, dataloader, statistics, plot, options
from util import array_operation as arr
from models import creatnet, core
# -----------------------------Init-----------------------------
opt = options.Options()
opt.parser.add_argument('--rec_tmp',
type=str,
default='./server_data/rec_data',
help='')
opt = opt.getparse()
opt.k_fold = 0
opt.save_dir = './checkpoints'
util.makedirs(opt.save_dir)
util.makedirs(opt.rec_tmp)
# -----------------------------Load original data-----------------------------
signals, labels = dataloader.loaddataset(opt)
ori_signals_train,ori_labels_train,ori_signals_eval,ori_labels_eval = \
signals[:opt.fold_index[0]].copy(),labels[:opt.fold_index[0]].copy(),signals[opt.fold_index[0]:].copy(),labels[opt.fold_index[0]:].copy()
label_cnt, label_cnt_per, label_num = statistics.label_statistics(labels)
opt = options.get_auto_options(opt, label_cnt_per, label_num,
import os
import time
import numpy as np
import torch
from torch import nn, optim
import warnings
warnings.filterwarnings("ignore")
from util import util, plot, options
from data import augmenter, transforms, dataloader, statistics
import core
opt = options.Options().getparse()
"""Use your own data to train
* 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.
"""
#----------------------------Load Data----------------------------
t1 = time.time()
signals, labels = dataloader.loaddataset(opt)
if opt.gan:
signals, labels = augmenter.dcgan(opt, signals, labels)
label_cnt, label_cnt_per, label_num = statistics.label_statistics(labels)