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import os
import csv
import torch
import random
import numpy as np
from torch.autograd import Variable
from model.tf_model import subsequent_mask
from torch.utils.data import Dataset
class Batch:
"Object for holding a batch of data with mask during training."
def __init__(self, src, trg=None, ys=None, pad=0):
self.src = src
self.ys = ys
self.src_len = src[:, 0, :]
self.src_mask = (self.src_len != pad).unsqueeze(-2)
if trg is not None:
# ------for EEG-------
self.trg = trg[:, :, :-1]
self.trg_len = trg[:, 0, :]
self.trg_x = self.trg_len[:, :-1]
self.trg_y = self.trg_len[:, 1:]
# ------for NLP-------
#self.trg = trg[:, :-1]
#self.trg_x = trg[:, :-1]
#self.trg_y = trg[:, 1:]
self.trg_mask = \
self.make_std_mask(self.trg_x, pad)
self.ntokens = (self.trg_y != pad).data.sum()
@staticmethod
def make_std_mask(tgt, pad):
"Create a mask to hide padding and future words."
tgt_mask = (tgt != pad).unsqueeze(-2)
tgt_mask = tgt_mask & Variable(
subsequent_mask(tgt.size(-1)).type_as(tgt_mask.data))
return tgt_mask
def data_gen(V, batch, nbatches):
"Generate random data for a src-tgt copy task."
for i in range(nbatches):
data = torch.from_numpy(np.random.randint(1, V, size=(batch, 10)))
data[:, 0] = 1
src = Variable(data, requires_grad=False)
tgt = Variable(data, requires_grad=False)
yield Batch(src, tgt, 0)
def data_load(train_loader):
print("data_load:", len(train_loader))
for i, (attr, target, ys) in enumerate(train_loader):
#src = Variable(attr, requires_grad=False)
#tgt = Variable(target, requires_grad=False)
#print("data_load1:", attr.shape)
#print("data_load2:", target.shape)
src = attr.cuda()
tgt = target.cuda()
ys = ys.cuda()
yield Batch(src, tgt, ys, 0)
class preDataset(Dataset):
def __init__(self, mode, train_len=870300, block_num=1):
self.sample_rate = 256
self.lenth = 870300 #train_len
self.lenthtest = 3600
self.lenthval = 3500
self.mode = mode
self.block_num = block_num
def __len__(self):
if self.mode == 2:
return self.lenthval
elif self.mode == 1:
return self.lenthtest
else:
return self.lenth
def __getitem__(self, idx):
'''
:param idx:
:return:
'''
data_mode = ["Brain", "ChannelNoise", "Eye", "Heart", "LineNoise", "Muscle", "Other"]
## step 1 locate idx into dataset
dataset = ["Hyperscanning_navigation", "Hyperscanning_slapjack", "Lane_keeping"]
if idx < 249816:
now_idx = idx
dataloc = 0
elif idx >= 249816 and idx < 506365:
now_idx = idx - 249816
dataloc = 1
elif idx >= 506365:
now_idx = idx - 506365
dataloc = 2
## step 2 read data
folder_name = './MetaPreTrain/' + dataset[dataloc] + '/3_ICA/'
allFileList = os.listdir(folder_name)
file_name1 = folder_name + allFileList[now_idx]
#print("preDataset1: ", allFileList[now_idx])
## get after 4 sec data
string_array = allFileList[now_idx]
parts = string_array[0].split('_')
numeric_part = parts[-1].split('.')[0]
new_numeric_part = str(int(numeric_part) + 4)
parts[-1] = new_numeric_part + '.csv'
string_array = '_'.join(parts)
file_name2 = folder_name + string_array
#print("preDataset2: ", string_array)
try:
data_nosie = self.read_train_data(file_name1)
data_clean = self.read_train_data(file_name2)
except:
data_nosie = self.read_train_data(file_name1)
data_clean = data_nosie
## step 3 signal normalize
max_num = np.max(data_nosie)
data_avg = np.average(data_nosie)
data_std = np.std(data_nosie)
if int(data_std) != 0:
target = np.array((data_clean - data_avg) / data_std).astype(np.float64)
attr = np.array((data_nosie - data_avg) / data_std).astype(np.float64)
else:
target = np.array(data_clean - data_avg).astype(np.float64)
attr = np.array(data_nosie - data_avg).astype(np.float64)
## step 4 deep copy & return
#target = target.copy()
target = torch.FloatTensor(target)
#attr = attr.copy()
attr = torch.FloatTensor(attr)
ys = torch.ones(30, 1023).fill_(1).type_as(attr)
return attr, target, ys
def read_train_data(self, file_name):
with open(file_name, 'r', newline='') as f:
lines = csv.reader(f)
data = []
for line in lines:
data.append(line)
new_data = np.array(data).astype(np.float64)
return new_data
class myDataset(Dataset):
def __init__(self, mode, train_len=0, block_num=1):
self.sample_rate = 256
self.lenth = train_len
self.lenthtest = 3600
self.lenthval = 3500
self.mode = mode
self.block_num = block_num
def __len__(self):
if self.mode == 2:
return self.lenthval
elif self.mode == 1:
return self.lenthtest
else:
return self.lenth
def __getitem__(self, idx):
'''
:param idx:
:return:
'''
data_mode = ["Brain", "ChannelNoise", "Eye", "Heart", "LineNoise", "Muscle", "Other"]
if self.mode == 2:
allFileList = os.listdir("./Real_EEG/val/Brain/")
file_name = './Real_EEG/val/Brain/' + allFileList[idx]
data_clean = self.read_train_data(file_name)
for i in range(7):
file_name = './Real_EEG/val/' + data_mode[random.randint(0, 6)] + '/' + allFileList[idx]
if os.path.isfile(file_name):
data_nosie = self.read_train_data(file_name)
break
else:
data_nosie = data_clean
elif self.mode == 1:
allFileList = os.listdir("./Real_EEG/test/Brain/")
file_name = './Real_EEG/test/Brain/' + allFileList[idx]
data_clean = self.read_train_data(file_name)
for i in range(7):
file_name = './Real_EEG/test/' + data_mode[random.randint(0, 6)] + '/' + allFileList[idx]
if os.path.isfile(file_name):
data_nosie = self.read_train_data(file_name)
break
else:
data_nosie = data_clean
else:
allFileList = os.listdir("./Real_EEG/train/Brain/")
file_name = './Real_EEG/train/Brain/' + allFileList[idx]
#print("dataloader: ", file_name)
data_clean = self.read_train_data(file_name)
for i in range(7):
file_name = './Real_EEG/train/' + data_mode[random.randint(0, 6)] + '/' + allFileList[idx]
if os.path.isfile(file_name):
data_nosie = self.read_train_data(file_name)
break
else:
data_nosie = data_clean
#print(file_name)
#print("data_set", noise.shape)
max_num = np.max(data_nosie)
data_avg = np.average(data_nosie)
data_std = np.std(data_nosie)
#max_num = 100
#print("max_num: ", max_num)
#target = np.array(data / max_num).astype(np.float)
if int(data_std) != 0:
target = np.array((data_clean - data_avg) / data_std).astype(np.float64)
attr = np.array((data_nosie - data_avg) / data_std).astype(np.float64)
else:
target = np.array(data_clean - data_avg).astype(np.float64)
attr = np.array(data_nosie - data_avg).astype(np.float64)
## step 4 deep copy & return
# target = target.copy()
target = torch.FloatTensor(target)
# attr = attr.copy()
attr = torch.FloatTensor(attr)
ys = torch.ones(30, 1023).fill_(1).type_as(attr)
return attr, target, ys
def read_train_data(self, file_name):
with open(file_name, 'r', newline='') as f:
lines = csv.reader(f)
data = []
for line in lines:
data.append(line)
new_data = np.array(data).astype(np.float64)
''' for training 19 channels
row = np.array([0,1,2,3,4,5,6,12,13,14,15,16,22,23,24,25,26,27,29])
new_data = []
for i in range(19):
#print(i, row[i])
#print(data[row[i]].shape)
new_data.append(data[row[i]])
new_data = np.array(new_data).astype(np.float)
'''
# data = data.T
return new_data |